{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FSRS4Anki Optimizer Power Difficulty\n",
    "\n",
    "[![open in colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/open-spaced-repetition/fsrs4anki/blob/main/archive/candidate/power_difficulty.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here are some settings that you need to replace before running this optimizer.\n",
    "\n",
    "filename = \"../collection-2022-09-18@13-21-58.colpkg\"\n",
    "# If you upload deck file, replace it with your deck filename. E.g., ALL__Learning.apkg\n",
    "# If you upload collection file, replace it with your colpgk filename. E.g., collection-2022-09-18@13-21-58.colpkg\n",
    "\n",
    "# Replace it with your timezone. I'm in China, so I use Asia/Shanghai.\n",
    "# You can find your timezone here: https://gist.github.com/heyalexej/8bf688fd67d7199be4a1682b3eec7568\n",
    "timezone = 'Asia/Shanghai'\n",
    "\n",
    "# Replace it with your Anki's setting in Preferences -> Scheduling.\n",
    "next_day_starts_at = 4\n",
    "\n",
    "# Replace it if you don't want the optimizer to use the review logs before a specific date.\n",
    "revlog_start_date = \"2006-10-05\"\n",
    "\n",
    "# Set it to True if you don't want the optimizer to use the review logs from suspended cards.\n",
    "filter_out_suspended_cards = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import zipfile\n",
    "import sqlite3\n",
    "import time\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import math\n",
    "from typing import List, Optional\n",
    "from datetime import timedelta, datetime\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "import torch\n",
    "from torch import nn\n",
    "from torch import Tensor\n",
    "from torch.utils.data import Dataset, DataLoader, Sampler\n",
    "from torch.nn.utils.rnn import pad_sequence, pack_padded_sequence, pad_packed_sequence\n",
    "from sklearn.model_selection import StratifiedGroupKFold\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from itertools import accumulate\n",
    "from tqdm.auto import tqdm\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
    "tqdm.pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deck file extracted successfully!\n",
      "revlog.csv saved.\n",
      "Trainset saved.\n",
      "Retention calculated.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "339237265c0c4b0e931eb704ba61dbe1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/26286 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stability calculated.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a62e54aeed02444abd698cb27eb9eb60",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "analysis:   0%|          | 0/489 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analysis saved!\n",
      "1:again, 2:hard, 3:good, 4:easy\n",
      "      r_history  avg_interval  avg_retention  stability  factor  group_cnt\n",
      "              1           1.1          0.892        1.0     inf       2063\n",
      "            1,3           3.1          0.920        4.0    4.00       1600\n",
      "          1,3,3           7.1          0.910        7.9    1.98       1320\n",
      "        1,3,3,3          16.6          0.862       10.8    1.37        986\n",
      "      1,3,3,3,3          36.5          0.840       22.3    2.06        659\n",
      "    1,3,3,3,3,3          77.0          0.861       36.4    1.63        358\n",
      "  1,3,3,3,3,3,3         117.9          0.906       38.5    1.06        177\n",
      "              2           1.0          0.902        1.1     inf        240\n",
      "            2,3           3.5          0.946        8.2    7.45        201\n",
      "          2,3,3          11.4          0.890        7.6    0.93        162\n",
      "              3           1.1          0.977        5.0     inf       4669\n",
      "            3,3           3.3          0.967       12.0    2.40       4211\n",
      "          3,3,3           8.1          0.960       19.9    1.66       3757\n",
      "        3,3,3,3          18.3          0.941       41.5    2.09       2834\n",
      "      3,3,3,3,3          35.2          0.930       50.5    1.22       1761\n",
      "    3,3,3,3,3,3          64.9          0.929       97.9    1.94       1102\n",
      "  3,3,3,3,3,3,3         100.9          0.949      143.0    1.46        530\n",
      "3,3,3,3,3,3,3,3         133.6          0.990     1821.9   12.74        131\n",
      "              4           2.0          0.976       10.7     inf       2789\n",
      "            4,3           3.7          0.976       20.4    1.91       2293\n",
      "          4,3,3           8.3          0.967       32.5    1.59       2051\n",
      "        4,3,3,3          19.8          0.958       54.5    1.68       1700\n",
      "      4,3,3,3,3          41.0          0.958       99.5    1.83       1148\n",
      "    4,3,3,3,3,3          68.1          0.956      116.1    1.17        521\n",
      "  4,3,3,3,3,3,3          78.5          0.979      110.7    0.95        248\n",
      "4,3,3,3,3,3,3,3         114.1          0.984      177.8    1.61        166\n"
     ]
    }
   ],
   "source": [
    "\"\"\"Step 1\"\"\"\n",
    "# Extract the collection file or deck file to get the .anki21 database.\n",
    "with zipfile.ZipFile(f'{filename}', 'r') as zip_ref:\n",
    "    zip_ref.extractall('./')\n",
    "    print(\"Deck file extracted successfully!\")\n",
    "\n",
    "\"\"\"Step 2\"\"\"\n",
    "if os.path.isfile(\"collection.anki21b\"):\n",
    "    os.remove(\"collection.anki21b\")\n",
    "    raise Exception(\n",
    "        \"Please export the file with `support older Anki versions` if you use the latest version of Anki.\")\n",
    "elif os.path.isfile(\"collection.anki21\"):\n",
    "    con = sqlite3.connect(\"collection.anki21\")\n",
    "elif os.path.isfile(\"collection.anki2\"):\n",
    "    con = sqlite3.connect(\"collection.anki2\")\n",
    "else:\n",
    "    raise Exception(\"Collection not exist!\")\n",
    "cur = con.cursor()\n",
    "res = cur.execute(f\"\"\"\n",
    "SELECT *\n",
    "FROM revlog\n",
    "WHERE cid IN (\n",
    "    SELECT id\n",
    "    FROM cards\n",
    "    {\"WHERE queue != -1\" if filter_out_suspended_cards else \"\"}\n",
    ")\n",
    "\"\"\"\n",
    ")\n",
    "revlog = res.fetchall()\n",
    "if len(revlog) == 0:\n",
    "    raise Exception(\"No review log found!\")\n",
    "df = pd.DataFrame(revlog)\n",
    "df.columns = ['id', 'cid', 'usn', 'r', 'ivl', 'last_lvl', 'factor', 'time', 'type']\n",
    "df = df[(df['cid'] <= time.time() * 1000) &\n",
    "        (df['id'] <= time.time() * 1000)].copy()\n",
    "\n",
    "df_set_due_date = df[(df['type'] == 4) & (df['ivl'] > 0)]\n",
    "df.drop(df_set_due_date.index, inplace=True)\n",
    "\n",
    "df['create_date'] = pd.to_datetime(df['cid'] // 1000, unit='s')\n",
    "df['create_date'] = df['create_date'].dt.tz_localize('UTC').dt.tz_convert(timezone)\n",
    "df['review_date'] = pd.to_datetime(df['id'] // 1000, unit='s')\n",
    "df['review_date'] = df['review_date'].dt.tz_localize('UTC').dt.tz_convert(timezone)\n",
    "df.drop(df[df['review_date'].dt.year < 2006].index, inplace=True)\n",
    "df.sort_values(by=['cid', 'id'], inplace=True, ignore_index=True)\n",
    "\n",
    "df['is_learn_start'] = (df['type'] == 0) & (df['type'].shift() != 0)\n",
    "df['sequence_group'] = df['is_learn_start'].cumsum()\n",
    "last_learn_start = df[df['is_learn_start']].groupby('cid')['sequence_group'].last()\n",
    "df['last_learn_start'] = df['cid'].map(last_learn_start).fillna(0).astype(int)\n",
    "df['mask'] = df['last_learn_start'] <= df['sequence_group']\n",
    "df = df[df['mask'] == True].copy()\n",
    "df.drop(columns=['is_learn_start', 'sequence_group', 'last_learn_start', 'mask'], inplace=True)\n",
    "df = df[(df['type'] != 4)].copy()\n",
    "\n",
    "type_sequence = np.array(df['type'])\n",
    "time_sequence = np.array(df['time'])\n",
    "df.to_csv(\"revlog.csv\", index=False)\n",
    "print(\"revlog.csv saved.\")\n",
    "\n",
    "df = df[(df['type'] != 3) | (df['factor'] != 0)].copy()\n",
    "df['real_days'] = df['review_date'] - timedelta(hours=int(next_day_starts_at))\n",
    "df['real_days'] = pd.DatetimeIndex(df['real_days'].dt.floor('D', ambiguous='infer', nonexistent='shift_forward')).to_julian_date()\n",
    "df.drop_duplicates(['cid', 'real_days'], keep='first', inplace=True)\n",
    "df['delta_t'] = df.real_days.diff()\n",
    "df.dropna(inplace=True)\n",
    "df['i'] = df.groupby('cid').cumcount() + 1\n",
    "df.loc[df['i'] == 1, 'delta_t'] = 0\n",
    "df = df.groupby('cid').filter(lambda group: group['type'].iloc[0] == 0)\n",
    "df['prev_type'] = df.groupby('cid')['type'].shift(1).fillna(0).astype(int)\n",
    "df['helper'] = ((df['type'] == 0) & ((df['prev_type'] == 1) | (df['prev_type'] == 2)) & (df['i'] > 1)).astype(int)\n",
    "df['helper'] = df.groupby('cid')['helper'].cumsum()\n",
    "df = df[df['helper'] == 0]\n",
    "del df['prev_type']\n",
    "del df['helper']\n",
    "\n",
    "def cum_concat(x):\n",
    "    return list(accumulate(x))\n",
    "\n",
    "t_history = df.groupby('cid', group_keys=False)['delta_t'].apply(lambda x: cum_concat([[int(i)] for i in x]))\n",
    "df['t_history']=[','.join(map(str, item[:-1])) for sublist in t_history for item in sublist]\n",
    "r_history = df.groupby('cid', group_keys=False)['r'].apply(lambda x: cum_concat([[i] for i in x]))\n",
    "df['r_history']=[','.join(map(str, item[:-1])) for sublist in r_history for item in sublist]\n",
    "df = df.groupby('cid').filter(lambda group: group['id'].min() > time.mktime(datetime.strptime(revlog_start_date, \"%Y-%m-%d\").timetuple()) * 1000)\n",
    "df['y'] = df['r'].map(lambda x: {1: 0, 2: 1, 3: 1, 4: 1}[x])\n",
    "df.to_csv('revlog_history.tsv', sep=\"\\t\", index=False)\n",
    "print(\"Trainset saved.\")\n",
    "\n",
    "df['retention'] = df.groupby(by=['r_history', 'delta_t'], group_keys=False)['y'].transform('mean')\n",
    "df['total_cnt'] = df.groupby(by=['r_history', 'delta_t'], group_keys=False)['id'].transform('count')\n",
    "print(\"Retention calculated.\")\n",
    "\n",
    "df = df.drop(columns=['id', 'cid', 'usn', 'ivl', 'last_lvl', 'factor', 'time', 'type', 'create_date', 'review_date', 'real_days', 'r', 't_history', 'y'])\n",
    "df.drop_duplicates(inplace=True)\n",
    "df['retention'] = df['retention'].map(lambda x: max(min(0.99, x), 0.01))\n",
    "\n",
    "def cal_stability(group: pd.DataFrame) -> pd.DataFrame:\n",
    "    group_cnt = sum(group['total_cnt'])\n",
    "    if group_cnt < 10:\n",
    "        return pd.DataFrame()\n",
    "    group['group_cnt'] = group_cnt\n",
    "    if group['i'].values[0] > 1:\n",
    "        r_ivl_cnt = sum(group['delta_t'] * group['retention'].map(np.log) * pow(group['total_cnt'], 2))\n",
    "        ivl_ivl_cnt = sum(group['delta_t'].map(lambda x: x ** 2) * pow(group['total_cnt'], 2))\n",
    "        group['stability'] = round(np.log(0.9) / (r_ivl_cnt / ivl_ivl_cnt), 1)\n",
    "    else:\n",
    "        group['stability'] = 0.0\n",
    "    group['avg_retention'] = round(sum(group['retention'] * pow(group['total_cnt'], 2)) / sum(pow(group['total_cnt'], 2)), 3)\n",
    "    group['avg_interval'] = round(sum(group['delta_t'] * pow(group['total_cnt'], 2)) / sum(pow(group['total_cnt'], 2)), 1)\n",
    "    del group['total_cnt']\n",
    "    del group['retention']\n",
    "    del group['delta_t']\n",
    "    return group\n",
    "\n",
    "df = df.groupby(by=['r_history'], group_keys=False).progress_apply(cal_stability)\n",
    "print(\"Stability calculated.\")\n",
    "df.reset_index(drop = True, inplace = True)\n",
    "df.drop_duplicates(inplace=True)\n",
    "df.sort_values(by=['r_history'], inplace=True, ignore_index=True)\n",
    "\n",
    "if df.shape[0] > 0:\n",
    "    for idx in tqdm(df.index, desc=\"analysis\"):\n",
    "        item = df.loc[idx]\n",
    "        index = df[(df['i'] == item['i'] + 1) & (df['r_history'].str.startswith(item['r_history']))].index\n",
    "        df.loc[index, 'last_stability'] = item['stability']\n",
    "    df['factor'] = round(df['stability'] / df['last_stability'], 2)\n",
    "    df = df[(df['i'] >= 2) & (df['group_cnt'] >= 100)].copy()\n",
    "    df['last_recall'] = df['r_history'].map(lambda x: x[-1])\n",
    "    df = df[df.groupby(['i', 'r_history'], group_keys=False)['group_cnt'].transform(max) == df['group_cnt']]\n",
    "    df.to_csv('./stability_for_analysis.tsv', sep='\\t', index=None)\n",
    "    print(\"Analysis saved!\")\n",
    "    caption = \"1:again, 2:hard, 3:good, 4:easy\\n\"\n",
    "    analysis = df[df['r_history'].str.contains(r'^[1-4][^124]*$', regex=True)][['r_history', 'avg_interval', 'avg_retention', 'stability', 'factor', 'group_cnt']].to_string(index=False)\n",
    "    print(caption + analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_w = [1, 1, 5, -0.5, -0.5, 0.2, 1.4, -0.2, 0.8, 2, -0.2, 0.2, 1, 1]\n",
    "\n",
    "class FSRS(nn.Module):\n",
    "    def __init__(self, w: List[float]):\n",
    "        super(FSRS, self).__init__()\n",
    "        self.w = nn.Parameter(torch.tensor(w, dtype=torch.float32))\n",
    "\n",
    "    def stability_after_success(self, state: Tensor, new_d: Tensor, r: Tensor) -> Tensor:\n",
    "        new_s = state[:,0] * (1 + torch.exp(self.w[6]) *\n",
    "                        (10 * torch.pow(new_d, -self.w[13])) *\n",
    "                        torch.pow(state[:,0], self.w[7]) *\n",
    "                        (torch.exp((1 - r) * self.w[8]) - 1))\n",
    "        return new_s\n",
    "\n",
    "    def stability_after_failure(self, state: Tensor, new_d: Tensor, r: Tensor) -> Tensor:\n",
    "        new_s = self.w[9] * torch.pow(new_d, self.w[10]) * torch.pow(\n",
    "            state[:,0], self.w[11]) * torch.exp((1 - r) * self.w[12])\n",
    "        return new_s\n",
    "\n",
    "    def step(self, X: Tensor, state: Tensor) -> Tensor:\n",
    "        '''\n",
    "        :param X: shape[batch_size, 2], X[:,0] is elapsed time, X[:,1] is rating\n",
    "        :param state: shape[batch_size, 2], state[:,0] is stability, state[:,1] is difficulty\n",
    "        :return state:\n",
    "        '''\n",
    "        if torch.equal(state, torch.zeros_like(state)):\n",
    "            # first learn, init memory states\n",
    "            new_s = self.w[0] + self.w[1] * (X[:,1] - 1)\n",
    "            new_d = self.w[2] + self.w[3] * (X[:,1] - 3)\n",
    "            new_d = new_d.clamp(1, 15)\n",
    "        else:\n",
    "            r = torch.exp(np.log(0.9) * X[:,0] / state[:,0])\n",
    "            new_d = state[:,1] + self.w[4] * (X[:,1] - 3)\n",
    "            new_d = self.mean_reversion(self.w[2], new_d)\n",
    "            new_d = new_d.clamp(1, 15)\n",
    "            condition = X[:,1] > 1\n",
    "            new_s = torch.where(condition, self.stability_after_success(state, new_d, r), self.stability_after_failure(state, new_d, r))\n",
    "        new_s = new_s.clamp(0.1, 36500)\n",
    "        return torch.stack([new_s, new_d], dim=1)\n",
    "\n",
    "    def forward(self, inputs: Tensor, state: Optional[Tensor]=None) -> Tensor:\n",
    "        '''\n",
    "        :param inputs: shape[seq_len, batch_size, 2]\n",
    "        '''\n",
    "        if state is None:\n",
    "            state = torch.zeros((inputs.shape[1], 2))\n",
    "        outputs = []\n",
    "        for X in inputs:\n",
    "            state = self.step(X, state)\n",
    "            outputs.append(state)\n",
    "        return torch.stack(outputs), state\n",
    "\n",
    "    def mean_reversion(self, init: Tensor, current: Tensor) -> Tensor:\n",
    "        return self.w[5] * init + (1-self.w[5]) * current\n",
    "\n",
    "class WeightClipper:\n",
    "    def __init__(self, frequency: int=1):\n",
    "        self.frequency = frequency\n",
    "\n",
    "    def __call__(self, module):\n",
    "        if hasattr(module, 'w'):\n",
    "            w = module.w.data\n",
    "            w[0] = w[0].clamp(0.1, 10)\n",
    "            w[1] = w[1].clamp(1, 15)\n",
    "            w[2] = w[2].clamp(1, 10)\n",
    "            w[3] = w[3].clamp(-5, -0.1)\n",
    "            w[4] = w[4].clamp(-5, -0.1)\n",
    "            w[5] = w[5].clamp(0.05, 0.5)\n",
    "            w[6] = w[6].clamp(0, 2)\n",
    "            w[7] = w[7].clamp(-0.8, -0.15)\n",
    "            w[8] = w[8].clamp(0.01, 1.5)\n",
    "            w[9] = w[9].clamp(0.5, 5)\n",
    "            w[10] = w[10].clamp(-2, -0.01)\n",
    "            w[11] = w[11].clamp(0.01, 0.9)\n",
    "            w[12] = w[12].clamp(0.01, 2)\n",
    "            w[13] = w[13].clamp(0.1, 2)\n",
    "            module.w.data = w\n",
    "\n",
    "def lineToTensor(line: str) -> Tensor:\n",
    "    ivl = line[0].split(',')\n",
    "    response = line[1].split(',')\n",
    "    tensor = torch.zeros(len(response), 2)\n",
    "    for li, response in enumerate(response):\n",
    "        tensor[li][0] = int(ivl[li])\n",
    "        tensor[li][1] = int(response)\n",
    "    return tensor\n",
    "\n",
    "class RevlogDataset(Dataset):\n",
    "    def __init__(self, dataframe: pd.DataFrame):\n",
    "        if dataframe.empty:\n",
    "            raise ValueError('Training data is inadequate.')\n",
    "        padded = pad_sequence(dataframe['tensor'].to_list(), batch_first=True, padding_value=0)\n",
    "        self.x_train = padded.int()\n",
    "        self.t_train = torch.tensor(dataframe['delta_t'].values, dtype=torch.int)\n",
    "        self.y_train = torch.tensor(dataframe['y'].values, dtype=torch.float)\n",
    "        self.seq_len = torch.tensor(dataframe['tensor'].map(len).values, dtype=torch.long)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.x_train[idx], self.t_train[idx], self.y_train[idx], self.seq_len[idx]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.y_train)\n",
    "\n",
    "class RevlogSampler(Sampler[List[int]]):\n",
    "    def __init__(self, data_source: RevlogDataset, batch_size: int):\n",
    "        self.data_source = data_source\n",
    "        self.batch_size = batch_size\n",
    "        lengths = np.array(data_source.seq_len)\n",
    "        indices = np.argsort(lengths)\n",
    "        full_batches, remainder = divmod(indices.size, self.batch_size)\n",
    "        if full_batches > 0:\n",
    "            if remainder == 0:\n",
    "                self.batch_indices = np.split(indices, full_batches)\n",
    "            else:\n",
    "                self.batch_indices = np.split(indices[:-remainder], full_batches)\n",
    "        else:\n",
    "            self.batch_indices = []\n",
    "        if remainder > 0:\n",
    "            self.batch_indices.append(indices[-remainder:])\n",
    "        self.batch_nums = len(self.batch_indices)\n",
    "        # seed = int(torch.empty((), dtype=torch.int64).random_().item())\n",
    "        seed = 2023\n",
    "        self.generator = torch.Generator()\n",
    "        self.generator.manual_seed(seed)\n",
    "\n",
    "    def __iter__(self):\n",
    "        yield from (self.batch_indices[idx] for idx in torch.randperm(self.batch_nums, generator=self.generator).tolist())\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data_source)\n",
    "\n",
    "\n",
    "def collate_fn(batch):\n",
    "    sequences, delta_ts, labels, seq_lens = zip(*batch)\n",
    "    sequences_packed = pack_padded_sequence(torch.stack(sequences, dim=1), lengths=torch.stack(seq_lens), batch_first=False, enforce_sorted=False)\n",
    "    sequences_padded, length = pad_packed_sequence(sequences_packed, batch_first=False)\n",
    "    sequences_padded = torch.as_tensor(sequences_padded)\n",
    "    seq_lens = torch.as_tensor(length)\n",
    "    delta_ts = torch.as_tensor(delta_ts)\n",
    "    labels = torch.as_tensor(labels)\n",
    "    return sequences_padded, delta_ts, labels, seq_lens\n",
    "\n",
    "class Trainer:\n",
    "    def __init__(self, train_set: pd.DataFrame, test_set: pd.DataFrame, init_w: List[float], n_epoch: int=1, lr: float=1e-2, batch_size: int=256) -> None:\n",
    "        self.model = FSRS(init_w)\n",
    "        self.optimizer = torch.optim.Adam(self.model.parameters(), lr=lr)\n",
    "        self.clipper = WeightClipper()\n",
    "        self.batch_size = batch_size\n",
    "        self.build_dataset(train_set, test_set)\n",
    "        self.n_epoch = n_epoch\n",
    "        self.batch_nums = self.next_train_data_loader.batch_sampler.batch_nums\n",
    "        self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(self.optimizer, T_max=self.batch_nums * n_epoch)\n",
    "        self.avg_train_losses = []\n",
    "        self.avg_eval_losses = []\n",
    "        self.loss_fn = nn.BCELoss(reduction='sum')\n",
    "\n",
    "    def build_dataset(self, train_set: pd.DataFrame, test_set: pd.DataFrame):\n",
    "        pre_train_set = train_set[train_set['i'] == 2]\n",
    "        self.pre_train_set = RevlogDataset(pre_train_set)\n",
    "        sampler = RevlogSampler(self.pre_train_set, batch_size=self.batch_size)\n",
    "        self.pre_train_data_loader = DataLoader(self.pre_train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        next_train_set = train_set[train_set['i'] > 2]\n",
    "        self.next_train_set = RevlogDataset(next_train_set)\n",
    "        sampler = RevlogSampler(self.next_train_set, batch_size=self.batch_size)\n",
    "        self.next_train_data_loader = DataLoader(self.next_train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        self.train_set = RevlogDataset(train_set)\n",
    "        sampler = RevlogSampler(self.train_set, batch_size=self.batch_size)\n",
    "        self.train_data_loader = DataLoader(self.train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        self.test_set = RevlogDataset(test_set)\n",
    "        sampler = RevlogSampler(self.test_set, batch_size=self.batch_size)\n",
    "        self.test_data_loader = DataLoader(self.test_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "        print(\"dataset built\")\n",
    "\n",
    "    def train(self, verbose: bool=True):\n",
    "        # pretrain\n",
    "        best_loss = np.inf\n",
    "        weighted_loss, w = self.eval()\n",
    "        if weighted_loss < best_loss:\n",
    "            best_loss = weighted_loss\n",
    "            best_w = w\n",
    "\n",
    "        pbar = tqdm(desc=\"pre-train\", colour=\"red\", total=len(self.pre_train_data_loader) * self.n_epoch)\n",
    "        for k in range(self.n_epoch):\n",
    "            for i, batch in enumerate(self.pre_train_data_loader):\n",
    "                self.model.train()\n",
    "                self.optimizer.zero_grad()\n",
    "                sequences, delta_ts, labels, seq_lens = batch\n",
    "                real_batch_size = seq_lens.shape[0]\n",
    "                outputs, _ = self.model(sequences)\n",
    "                stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "                retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "                loss = self.loss_fn(retentions, labels)\n",
    "                loss.backward()\n",
    "                self.optimizer.step()\n",
    "                self.model.apply(self.clipper)\n",
    "                pbar.update(n=real_batch_size)\n",
    "\n",
    "        pbar.close()\n",
    "        for name, param in self.model.named_parameters():\n",
    "            tqdm.write(f\"{name}: {list(map(lambda x: round(float(x), 4),param))}\")\n",
    "\n",
    "        epoch_len = len(self.next_train_data_loader)\n",
    "        pbar = tqdm(desc=\"train\", colour=\"red\", total=epoch_len*self.n_epoch)\n",
    "        print_len = max(self.batch_nums*self.n_epoch // 10, 1)\n",
    "        for k in range(self.n_epoch):\n",
    "            weighted_loss, w = self.eval()\n",
    "            if weighted_loss < best_loss:\n",
    "                best_loss = weighted_loss\n",
    "                best_w = w\n",
    "\n",
    "            for i, batch in enumerate(self.next_train_data_loader):\n",
    "                self.model.train()\n",
    "                self.optimizer.zero_grad()\n",
    "                sequences, delta_ts, labels, seq_lens = batch\n",
    "                real_batch_size = seq_lens.shape[0]\n",
    "                outputs, _ = self.model(sequences)\n",
    "                stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "                retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "                loss = self.loss_fn(retentions, labels)\n",
    "                loss.backward()\n",
    "                for param in self.model.parameters():\n",
    "                    param.grad[:2] = torch.zeros(2)\n",
    "                self.optimizer.step()\n",
    "                self.scheduler.step()\n",
    "                self.model.apply(self.clipper)\n",
    "                pbar.update(real_batch_size)\n",
    "\n",
    "                if verbose and (k * self.batch_nums + i + 1) % print_len == 0:\n",
    "                    tqdm.write(f\"iteration: {k * epoch_len + (i + 1) * self.batch_size}\")\n",
    "                    for name, param in self.model.named_parameters():\n",
    "                        tqdm.write(f\"{name}: {list(map(lambda x: round(float(x), 4),param))}\")\n",
    "        pbar.close()\n",
    "\n",
    "        weighted_loss, w = self.eval()\n",
    "        if weighted_loss < best_loss:\n",
    "            best_loss = weighted_loss\n",
    "            best_w = w\n",
    "\n",
    "        return best_w\n",
    "\n",
    "    def eval(self):\n",
    "        self.model.eval()\n",
    "        with torch.no_grad():\n",
    "            sequences, delta_ts, labels, seq_lens = self.train_set.x_train, self.train_set.t_train, self.train_set.y_train, self.train_set.seq_len\n",
    "            real_batch_size = seq_lens.shape[0]\n",
    "            outputs, _ = self.model(sequences.transpose(0, 1))\n",
    "            stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "            retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "            tran_loss = self.loss_fn(retentions, labels)/len(self.train_set)\n",
    "            self.avg_train_losses.append(tran_loss)\n",
    "            tqdm.write(f\"Loss in trainset: {tran_loss:.4f}\")\n",
    "\n",
    "            sequences, delta_ts, labels, seq_lens = self.test_set.x_train, self.test_set.t_train, self.test_set.y_train, self.test_set.seq_len\n",
    "            real_batch_size = seq_lens.shape[0]\n",
    "            outputs, _ = self.model(sequences.transpose(0, 1))\n",
    "            stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "            retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "            test_loss = self.loss_fn(retentions, labels)/len(self.test_set)\n",
    "            self.avg_eval_losses.append(test_loss)\n",
    "            tqdm.write(f\"Loss in testset: {test_loss:.4f}\")\n",
    "\n",
    "            w = list(map(lambda x: round(float(x), 4), dict(self.model.named_parameters())['w'].data))\n",
    "\n",
    "            weighted_loss = (tran_loss * len(self.train_set) + test_loss * len(self.test_set)) / (len(self.train_set) + len(self.test_set))\n",
    "\n",
    "            return weighted_loss, w\n",
    "\n",
    "    def plot(self):\n",
    "        fig = plt.figure()\n",
    "        ax = fig.gca()\n",
    "        ax.plot(self.avg_train_losses, label='train')\n",
    "        ax.plot(self.avg_eval_losses, label='test')\n",
    "        ax.set_xlabel('epoch')\n",
    "        ax.set_ylabel('loss')\n",
    "        ax.legend()\n",
    "        return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6ab9f241b17d444ea6f13a443b285433",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/88151 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensorized!\n",
      "TRAIN: 57372 TEST: 30779\n",
      "dataset built\n",
      "Loss in trainset: 0.3482\n",
      "Loss in testset: 0.3263\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "097a95dca8af4619aa322e145b71af1c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/15276 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [1.2588, 1.8518, 5.0, -0.5, -0.5, 0.2, 1.4, -0.2, 0.8, 2.0, -0.2, 0.2, 1.0, 1.0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1aba505cd84049d6a0913e41fbafd226",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/156840 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3395\n",
      "Loss in testset: 0.3183\n",
      "iteration: 15360\n",
      "w: [1.2327, 2.0554, 4.4505, -1.4702, -1.3508, 0.05, 1.6837, -0.15, 1.0793, 2.1041, -0.1491, 0.3925, 0.9271, 0.8857]\n",
      "iteration: 30720\n",
      "w: [1.2313, 2.0664, 3.8199, -2.3034, -1.6781, 0.05, 1.8446, -0.15, 1.1987, 2.1205, -0.1444, 0.3047, 0.9338, 0.9897]\n",
      "iteration: 46080\n",
      "w: [1.2312, 2.067, 3.3476, -2.3104, -1.9127, 0.05, 1.9218, -0.1625, 1.2606, 2.1406, -0.1577, 0.3759, 0.8867, 1.1111]\n",
      "Loss in trainset: 0.3179\n",
      "Loss in testset: 0.2981\n",
      "iteration: 60984\n",
      "w: [1.2312, 2.067, 3.2014, -2.1574, -2.0398, 0.0525, 1.931, -0.2035, 1.2601, 2.1111, -0.1951, 0.3634, 0.8795, 1.1851]\n",
      "iteration: 76344\n",
      "w: [1.2312, 2.067, 2.9968, -2.084, -1.9535, 0.1176, 2.0, -0.15, 1.3174, 2.1154, -0.1923, 0.2678, 0.8511, 1.1752]\n",
      "iteration: 91704\n",
      "w: [1.2312, 2.067, 2.9512, -2.1585, -2.0572, 0.1094, 1.9477, -0.2271, 1.2848, 2.162, -0.147, 0.3042, 0.7846, 1.2315]\n",
      "Loss in trainset: 0.3158\n",
      "Loss in testset: 0.2963\n",
      "iteration: 106608\n",
      "w: [1.2312, 2.067, 2.8415, -2.1646, -2.1068, 0.0891, 1.979, -0.2001, 1.3045, 2.1187, -0.1974, 0.3251, 0.6911, 1.233]\n",
      "iteration: 121968\n",
      "w: [1.2312, 2.067, 2.8301, -2.1735, -2.1386, 0.0848, 1.9662, -0.2112, 1.2893, 2.1335, -0.1816, 0.3315, 0.6707, 1.2556]\n",
      "iteration: 137328\n",
      "w: [1.2312, 2.067, 2.8012, -2.1815, -2.1447, 0.093, 1.9753, -0.1984, 1.2949, 2.1367, -0.1791, 0.3451, 0.6739, 1.2559]\n",
      "iteration: 152688\n",
      "w: [1.2312, 2.067, 2.7992, -2.182, -2.1492, 0.0867, 1.9733, -0.202, 1.2928, 2.1335, -0.1818, 0.3418, 0.6711, 1.2594]\n",
      "Loss in trainset: 0.3157\n",
      "Loss in testset: 0.2962\n",
      "TRAIN: 59696 TEST: 28455\n",
      "dataset built\n",
      "Loss in trainset: 0.3342\n",
      "Loss in testset: 0.3537\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5210b869228b46fc8970ff4c17c6a7eb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/22374 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [2.0143, 2.047, 5.0, -0.5, -0.5, 0.2, 1.4, -0.2, 0.8, 2.0, -0.2, 0.2, 1.0, 1.0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5ff8a10ab5fa4c6caa4ab99e5025e62f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/156714 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3224\n",
      "Loss in testset: 0.3432\n",
      "iteration: 15360\n",
      "w: [2.1098, 2.1547, 4.4018, -1.4446, -1.2905, 0.05, 1.7593, -0.15, 1.1483, 2.0491, -0.1856, 0.3362, 1.0903, 0.7997]\n",
      "iteration: 30720\n",
      "w: [2.1147, 2.1603, 3.9118, -2.3629, -1.6249, 0.05, 1.7986, -0.15, 1.1846, 2.0043, -0.2478, 0.3738, 1.0116, 0.9946]\n",
      "iteration: 46080\n",
      "w: [2.1149, 2.1605, 3.5556, -2.386, -1.7276, 0.05, 1.8372, -0.1599, 1.2121, 1.9916, -0.2662, 0.3427, 0.8373, 1.1209]\n",
      "Loss in trainset: 0.3035\n",
      "Loss in testset: 0.3245\n",
      "iteration: 60942\n",
      "w: [2.1149, 2.1605, 3.4956, -1.9506, -1.7928, 0.1599, 1.8568, -0.1781, 1.2227, 2.0529, -0.201, 0.3399, 0.8814, 1.1709]\n",
      "iteration: 76302\n",
      "w: [2.1149, 2.1605, 3.3032, -2.1036, -1.8771, 0.05, 1.8815, -0.158, 1.2438, 2.0571, -0.1956, 0.3532, 0.8102, 1.211]\n",
      "iteration: 91662\n",
      "w: [2.1149, 2.1605, 3.2165, -1.9811, -1.9068, 0.05, 1.931, -0.1584, 1.2806, 2.0451, -0.2109, 0.3398, 0.7538, 1.2048]\n",
      "Loss in trainset: 0.3024\n",
      "Loss in testset: 0.3235\n",
      "iteration: 106524\n",
      "w: [2.1149, 2.1605, 3.2192, -1.8781, -1.9419, 0.0665, 1.9348, -0.1683, 1.276, 2.0677, -0.1849, 0.3652, 0.7466, 1.2149]\n",
      "iteration: 121884\n",
      "w: [2.1149, 2.1605, 3.2146, -1.8923, -1.9759, 0.0714, 1.9371, -0.1683, 1.2739, 2.0598, -0.1901, 0.346, 0.7114, 1.2237]\n",
      "iteration: 137244\n",
      "w: [2.1149, 2.1605, 3.2185, -1.8901, -1.9796, 0.0729, 1.9278, -0.1753, 1.2634, 2.0677, -0.1812, 0.3541, 0.7138, 1.2373]\n",
      "iteration: 152604\n",
      "w: [2.1149, 2.1605, 3.2139, -1.8887, -1.9808, 0.0722, 1.9305, -0.174, 1.2654, 2.0672, -0.1813, 0.3539, 0.7127, 1.2365]\n",
      "Loss in trainset: 0.3024\n",
      "Loss in testset: 0.3234\n",
      "TRAIN: 59234 TEST: 28917\n",
      "dataset built\n",
      "Loss in trainset: 0.3394\n",
      "Loss in testset: 0.3427\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "54b9f88b90bf4d3198d6c1d45b5b40bd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/20916 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [1.2804, 1.8287, 5.0, -0.5, -0.5, 0.2, 1.4, -0.2, 0.8, 2.0, -0.2, 0.2, 1.0, 1.0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2c50ed3183a24ea79622d83bd8ea91d6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/156786 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3313\n",
      "Loss in testset: 0.3339\n",
      "iteration: 15360\n",
      "w: [1.2103, 1.9125, 4.4924, -1.6602, -1.3081, 0.05, 1.7084, -0.1717, 1.1074, 2.0222, -0.1699, 0.3367, 1.0296, 0.8351]\n",
      "iteration: 30720\n",
      "w: [1.2066, 1.9168, 4.0473, -2.462, -1.7657, 0.05, 1.7648, -0.15, 1.1522, 1.9404, -0.2707, 0.3531, 0.6969, 1.0039]\n",
      "iteration: 46080\n",
      "w: [1.2065, 1.917, 3.7108, -2.4948, -1.8982, 0.0576, 1.8639, -0.15, 1.2218, 2.0262, -0.1842, 0.3313, 0.5741, 1.0446]\n",
      "Loss in trainset: 0.3096\n",
      "Loss in testset: 0.3107\n",
      "iteration: 60966\n",
      "w: [1.2065, 1.917, 3.5191, -2.1896, -2.1132, 0.05, 1.8967, -0.1725, 1.2375, 2.0748, -0.1583, 0.3542, 0.5188, 1.1189]\n",
      "iteration: 76326\n",
      "w: [1.2065, 1.917, 3.3299, -2.1873, -2.1543, 0.0549, 1.9166, -0.1906, 1.2485, 2.0721, -0.152, 0.3022, 0.5479, 1.1724]\n",
      "iteration: 91686\n",
      "w: [1.2065, 1.917, 3.2347, -2.0987, -2.1789, 0.0698, 1.9685, -0.1728, 1.2862, 2.0839, -0.127, 0.2946, 0.5045, 1.1578]\n",
      "Loss in trainset: 0.3092\n",
      "Loss in testset: 0.3104\n",
      "iteration: 106572\n",
      "w: [1.2065, 1.917, 3.2223, -2.0662, -2.1888, 0.101, 1.9774, -0.1877, 1.2894, 2.0727, -0.1384, 0.3009, 0.4376, 1.1603]\n",
      "iteration: 121932\n",
      "w: [1.2065, 1.917, 3.1666, -2.0853, -2.2235, 0.0564, 1.9811, -0.1929, 1.2889, 2.0752, -0.1379, 0.316, 0.4195, 1.1839]\n",
      "iteration: 137292\n",
      "w: [1.2065, 1.917, 3.1814, -2.072, -2.2244, 0.0681, 1.9724, -0.202, 1.2803, 2.0814, -0.1308, 0.3254, 0.4169, 1.1922]\n",
      "iteration: 152652\n",
      "w: [1.2065, 1.917, 3.1786, -2.0678, -2.2256, 0.0685, 1.973, -0.2014, 1.2804, 2.0798, -0.1324, 0.3263, 0.4157, 1.1934]\n",
      "Loss in trainset: 0.3089\n",
      "Loss in testset: 0.3099\n",
      "\n",
      "Training finished!\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr: float = 4e-2\n",
    "n_epoch: int = 3\n",
    "n_splits: int = 3\n",
    "batch_size: int = 512\n",
    "verbose: bool = True\n",
    "\n",
    "dataset = pd.read_csv(\"./revlog_history.tsv\", sep='\\t', index_col=None, dtype={'r_history': str ,'t_history': str} )\n",
    "dataset = dataset[(dataset['i'] > 1) & (dataset['delta_t'] > 0) & (dataset['t_history'].str.count(',0') == 0)]\n",
    "if dataset.empty:\n",
    "    raise ValueError('Training data is inadequate.')\n",
    "dataset['tensor'] = dataset.progress_apply(lambda x: lineToTensor(list(zip([x['t_history']], [x['r_history']]))[0]), axis=1)\n",
    "dataset['group'] = dataset['r_history'] + dataset['t_history']\n",
    "print(\"Tensorized!\")\n",
    "\n",
    "n_pre_train_groups = len(dataset[dataset['i'] == 2]['group'].unique())\n",
    "if n_pre_train_groups < n_splits:\n",
    "    print(\"Not enough groups for pre-training. Splitting into {} folds.\".format(n_pre_train_groups))\n",
    "    n_splits = n_pre_train_groups\n",
    "\n",
    "w = []\n",
    "plots = []\n",
    "if n_splits > 1:\n",
    "    sgkf = StratifiedGroupKFold(n_splits=n_splits)\n",
    "    for train_index, test_index in sgkf.split(dataset, dataset['i'], dataset['group']):\n",
    "        print(\"TRAIN:\", len(train_index), \"TEST:\",  len(test_index))\n",
    "        train_set = dataset.iloc[train_index].copy()\n",
    "        test_set = dataset.iloc[test_index].copy()\n",
    "        trainer = Trainer(train_set, test_set, init_w, n_epoch=n_epoch, lr=lr, batch_size=batch_size)\n",
    "        w.append(trainer.train(verbose=verbose))\n",
    "        plots.append(trainer.plot())\n",
    "else:\n",
    "    trainer = Trainer(dataset, dataset, init_w, n_epoch=n_epoch, lr=lr, batch_size=batch_size)\n",
    "    w.append(trainer.train(verbose=verbose))\n",
    "    plots.append(trainer.plot())\n",
    "\n",
    "w = np.array(w)\n",
    "avg_w = np.round(np.mean(w, axis=0), 4)\n",
    "w = avg_w.tolist()\n",
    "print(\"\\nTraining finished!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.5175, 2.0482, 3.0638, -2.0461, -2.1186, 0.0757, 1.9589, -0.1924, 1.2796, 2.0934, -0.1652, 0.3406, 0.5997, 1.2298]\n",
      "1:again, 2:hard, 3:good, 4:easy\n",
      "\n",
      "first rating: 1\n",
      "rating history: 1,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0d,2d,3d,5d,9d,15d,25d,1.4m,2.2m,3.4m,5.2m\n",
      "difficulty history: 0,7.2,6.8,6.6,6.3,6.1,5.8,5.6,5.4,5.2,5.1\n",
      "\n",
      "first rating: 2\n",
      "rating history: 2,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0d,4d,8d,15d,28d,1.7m,2.9m,4.8m,7.9m,1.0y,1.6y\n",
      "difficulty history: 0,5.1,5.0,4.8,4.7,4.6,4.4,4.3,4.2,4.2,4.1\n",
      "\n",
      "first rating: 3\n",
      "rating history: 3,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0d,6d,16d,1.3m,2.9m,5.8m,11.1m,1.6y,2.8y,4.6y,7.3y\n",
      "difficulty history: 0,3.1,3.1,3.1,3.1,3.1,3.1,3.1,3.1,3.1,3.1\n",
      "\n",
      "first rating: 4\n",
      "rating history: 4,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0d,8d,1.7m,7.2m,1.9y,4.9y,10.7y,21.1y,37.9y,63.2y,99.7y\n",
      "difficulty history: 0,1.0,1.2,1.3,1.4,1.6,1.7,1.8,1.9,2.0,2.1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(w)\n",
    "\n",
    "class Collection:\n",
    "    def __init__(self, w: List[float]) -> None:\n",
    "        self.model = FSRS(w)\n",
    "        self.model.eval()\n",
    "\n",
    "    def predict(self, t_history: str, r_history: str):\n",
    "        with torch.no_grad():\n",
    "            line_tensor = lineToTensor(list(zip([t_history], [r_history]))[0]).unsqueeze(1)\n",
    "            output_t = self.model(line_tensor)\n",
    "            return output_t[-1][0]\n",
    "\n",
    "    def batch_predict(self, dataset):\n",
    "        fast_dataset = RevlogDataset(dataset)\n",
    "        with torch.no_grad():\n",
    "            outputs, _ = self.model(fast_dataset.x_train.transpose(0, 1))\n",
    "            stabilities, difficulties = outputs[fast_dataset.seq_len-1, torch.arange(len(fast_dataset))].transpose(0, 1)\n",
    "            return stabilities.tolist(), difficulties.tolist()\n",
    "        \n",
    "requestRetention = 0.9\n",
    "\n",
    "my_collection = Collection(w)\n",
    "preview_text = \"1:again, 2:hard, 3:good, 4:easy\\n\"\n",
    "for first_rating in (1,2,3,4):\n",
    "    preview_text += f'\\nfirst rating: {first_rating}\\n'\n",
    "    t_history = \"0\"\n",
    "    d_history = \"0\"\n",
    "    r_history = f\"{first_rating}\"  # the first rating of the new card\n",
    "    # print(\"stability, difficulty, lapses\")\n",
    "    for i in range(10):\n",
    "        states = my_collection.predict(t_history, r_history)\n",
    "        # print('{0:9.2f} {1:11.2f} {2:7.0f}'.format(\n",
    "            # *list(map(lambda x: round(float(x), 4), states))))\n",
    "        next_t = max(round(float(np.log(requestRetention)/np.log(0.9) * states[0])), 1)\n",
    "        difficulty = round(float(states[1]), 1)\n",
    "        t_history += f',{int(next_t)}'\n",
    "        d_history += f',{difficulty}'\n",
    "        r_history += f\",3\"\n",
    "    preview_text += f\"rating history: {r_history}\\n\"\n",
    "    preview_text += \"interval history: \" + \",\".join([f\"{ivl}d\" if ivl < 30 else f\"{ivl / 30:.1f}m\" if ivl < 365 else f\"{ivl / 365:.1f}y\" for ivl in map(int, t_history.split(','))]) + \"\\n\"\n",
    "    preview_text += f\"difficulty history: {d_history}\\n\"\n",
    "print(preview_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([5.6139, 3.0638])\n",
      "tensor([16.1380,  3.0638])\n",
      "tensor([39.0248,  3.0638])\n",
      "tensor([86.0988,  3.0638])\n",
      "tensor([175.2430,   3.0638])\n",
      "tensor([9.3678, 6.9802])\n",
      "tensor([ 3.2172, 10.6002])\n",
      "tensor([ 4.5777, 10.0297])\n",
      "tensor([6.8456, 9.5024])\n",
      "tensor([9.9785, 9.0150])\n",
      "tensor([14.4110,  8.5645])\n",
      "tensor([20.5564,  8.1481])\n",
      "rating history: 3,3,3,3,3,1,1,3,3,3,3,3\n",
      "interval history: 0,6,16,39,86,175,9,3,5,7,10,14,21\n",
      "difficulty history: 0,3.1,3.1,3.1,3.1,3.1,7.0,10.6,10.0,9.5,9.0,8.6,8.1\n"
     ]
    }
   ],
   "source": [
    "test_rating_sequence = \"3,3,3,3,3,1,1,3,3,3,3,3\"\n",
    "requestRetention = 0.9  # recommended setting: 0.8 ~ 0.9\n",
    "easyBonus = 1.3\n",
    "hardInterval = 1.2\n",
    "\n",
    "t_history = \"0\"\n",
    "d_history = \"0\"\n",
    "for i in range(len(test_rating_sequence.split(','))):\n",
    "    rating = test_rating_sequence[2*i]\n",
    "    last_t = int(t_history.split(',')[-1])\n",
    "    r_history = test_rating_sequence[:2*i+1]\n",
    "    states = my_collection.predict(t_history, r_history)\n",
    "    print(states)\n",
    "    next_t = max(1,round(float(np.log(requestRetention)/np.log(0.9) * states[0])))\n",
    "    if rating == '4':\n",
    "        next_t = round(next_t * easyBonus)\n",
    "    elif rating == '2':\n",
    "        next_t = round(last_t * hardInterval)\n",
    "    t_history += f',{int(next_t)}'\n",
    "    difficulty = round(float(states[1]), 1)\n",
    "    d_history += f',{difficulty}'\n",
    "preview_text = f\"rating history: {test_rating_sequence}\\n\"\n",
    "preview_text += f\"interval history: {t_history}\\n\"\n",
    "preview_text += f\"difficulty history: {d_history}\"\n",
    "print(preview_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss before: 0.3405, loss after: 0.3090, improvement: 0.0315\n"
     ]
    }
   ],
   "source": [
    "my_collection = Collection(init_w)\n",
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "dataset['stability'] = stabilities\n",
    "dataset['difficulty'] = difficulties\n",
    "dataset['p'] = np.exp(np.log(0.9) * dataset['delta_t'] / dataset['stability'])\n",
    "dataset['log_loss'] = dataset.apply(lambda row: - np.log(row['p']) if row['y'] == 1 else - np.log(1 - row['p']), axis=1)\n",
    "loss_before = dataset['log_loss'].mean()\n",
    "\n",
    "my_collection = Collection(w)\n",
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "dataset['stability'] = stabilities\n",
    "dataset['difficulty'] = difficulties\n",
    "dataset['p'] = np.exp(np.log(0.9) * dataset['delta_t'] / dataset['stability'])\n",
    "dataset['log_loss'] = dataset.apply(lambda row: - np.log(row['p']) if row['y'] == 1 else - np.log(1 - row['p']), axis=1)\n",
    "loss_after = dataset['log_loss'].mean()\n",
    "\n",
    "tmp = dataset.copy()\n",
    "tmp['stability'] = tmp['stability'].map(lambda x: round(x, 2))\n",
    "tmp['difficulty'] = tmp['difficulty'].map(lambda x: round(x, 2))\n",
    "tmp['p'] = tmp['p'].map(lambda x: round(x, 2))\n",
    "tmp['log_loss'] = tmp['log_loss'].map(lambda x: round(x, 2))\n",
    "tmp.rename(columns={\"r\": \"grade\", \"p\": \"retrievability\"}, inplace=True)\n",
    "tmp[['id', 'cid', 'review_date', 'r_history', 't_history', 'delta_t', 'grade', 'stability', 'difficulty', 'retrievability', 'log_loss']].to_csv(\"./evaluation.tsv\", sep='\\t', index=False)\n",
    "del tmp\n",
    "print(f\"loss before: {loss_before:.4f}, loss after: {loss_after:.4f}, improvement: {loss_before - loss_after:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared: 0.9182\n",
      "RMSE: 0.0205\n",
      "[0.13710937 0.84832369]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Last rating: 1\n",
      "R-squared: 0.2213\n",
      "RMSE: 0.0508\n",
      "[0.29929009 0.64272701]\n",
      "\n",
      "Last rating: 2\n",
      "R-squared: -0.8361\n",
      "RMSE: 0.0998\n",
      "[-0.74017593  1.70020281]\n",
      "\n",
      "Last rating: 3\n",
      "R-squared: 0.9124\n",
      "RMSE: 0.0236\n",
      "[0.09109916 0.91272787]\n",
      "\n",
      "Last rating: 4\n",
      "R-squared: -0.0162\n",
      "RMSE: 0.0407\n",
      "[0.44991024 0.54300112]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x1200 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# code from https://github.com/papousek/duolingo-halflife-regression/blob/master/evaluation.py\n",
    "def load_brier(predictions, real, bins=20):\n",
    "    counts = np.zeros(bins)\n",
    "    correct = np.zeros(bins)\n",
    "    prediction = np.zeros(bins)\n",
    "    for p, r in zip(predictions, real):\n",
    "        bin = min(int(p * bins), bins - 1)\n",
    "        counts[bin] += 1\n",
    "        correct[bin] += r\n",
    "        prediction[bin] += p\n",
    "    np.seterr(invalid='ignore')\n",
    "    prediction_means = prediction / counts\n",
    "    prediction_means[np.isnan(prediction_means)] = ((np.arange(bins) + 0.5) / bins)[np.isnan(prediction_means)]\n",
    "    correct_means = correct / counts\n",
    "    correct_means[np.isnan(correct_means)] = 0\n",
    "    size = len(predictions)\n",
    "    answer_mean = sum(correct) / size\n",
    "    return {\n",
    "        \"reliability\": sum(counts * (correct_means - prediction_means) ** 2) / size,\n",
    "        \"resolution\": sum(counts * (correct_means - answer_mean) ** 2) / size,\n",
    "        \"uncertainty\": answer_mean * (1 - answer_mean),\n",
    "        \"detail\": {\n",
    "            \"bin_count\": bins,\n",
    "            \"bin_counts\": list(counts),\n",
    "            \"bin_prediction_means\": list(prediction_means),\n",
    "            \"bin_correct_means\": list(correct_means),\n",
    "        }\n",
    "    }\n",
    "\n",
    "\n",
    "def plot_brier(predictions, real, bins=20):\n",
    "    brier = load_brier(predictions, real, bins=bins)\n",
    "    bin_prediction_means = brier['detail']['bin_prediction_means']\n",
    "    bin_correct_means = brier['detail']['bin_correct_means']\n",
    "    bin_counts = brier['detail']['bin_counts']\n",
    "    r2 = r2_score(bin_correct_means, bin_prediction_means, sample_weight=bin_counts)\n",
    "    rmse = mean_squared_error(bin_correct_means, bin_prediction_means, sample_weight=bin_counts, squared=False)\n",
    "    print(f\"R-squared: {r2:.4f}\")\n",
    "    print(f\"RMSE: {rmse:.4f}\")\n",
    "    ax = plt.gca()\n",
    "    ax.set_xlim([0, 1])\n",
    "    ax.set_ylim([0, 1])\n",
    "    plt.grid(True)\n",
    "    fit_wls = sm.WLS(bin_correct_means, sm.add_constant(bin_prediction_means), weights=bin_counts).fit()\n",
    "    print(fit_wls.params)\n",
    "    y_regression = [fit_wls.params[0] + fit_wls.params[1]*x for x in bin_prediction_means]\n",
    "    plt.plot(bin_prediction_means, y_regression, label='Weighted Least Squares Regression', color=\"green\")\n",
    "    plt.plot(bin_prediction_means, bin_correct_means, label='Actual Calibration', color=\"#1f77b4\")\n",
    "    plt.plot((0, 1), (0, 1), label='Perfect Calibration', color=\"#ff7f0e\")\n",
    "    bin_count = brier['detail']['bin_count']\n",
    "    counts = np.array(bin_counts)\n",
    "    bins = (np.arange(bin_count) + 0.5) / bin_count\n",
    "    plt.legend(loc='upper center')\n",
    "    plt.xlabel('Predicted R')\n",
    "    plt.ylabel('Actual R')\n",
    "    plt.twinx()\n",
    "    plt.ylabel('Number of reviews')\n",
    "    plt.bar(bins, counts, width=(0.8 / bin_count), ec='k', lw=.2, alpha=0.5, label='Number of reviews')\n",
    "    plt.legend(loc='lower center')\n",
    "\n",
    "\n",
    "plot_brier(dataset['p'], dataset['y'], bins=40)\n",
    "plt.show()\n",
    "plt.figure(figsize=(16, 12))\n",
    "for last_rating in (\"1\",\"2\",\"3\",\"4\"):\n",
    "    plt.subplot(2, 2, int(last_rating))\n",
    "    print(f\"\\nLast rating: {last_rating}\")\n",
    "    plot_brier(dataset[dataset['r_history'].str.endswith(last_rating)]['p'], dataset[dataset['r_history'].str.endswith(last_rating)]['y'], bins=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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y+wF/2B/Fh2tPkKfT87i/KwveaIqLXfH+FX2+kTf/RKew+WT0fSWJJ67KItqidEmSKB5JN1Kz2HPsDCs1n+OkZADQXDnFnw4TeU8zhi2J3lxPyeH99RH837aLvNSkKq8+7kstj+ItxC3EoyAjO49fDl9h4V8XuHjzbpdHDTQqhWp22TS3vkQj9UUctSo0DpWwdXTD3tkNJ1cPKrl6UMnNHY1dJcOs3YJ0OvQ3I4g/s4ekiD3YxBzGM/MCanSQheEAcvUqTuursV9Xl3P2wbzV8zVqVvMt/S9AAVZqFdNfbsSLc/9mw4lo/jh+nY4Nve/5PJ1Oz/9tPsP8HecB6Bpchc+6NbToH8/29b34fPNZ/j53k5TMHBxtrErUh/yRxPqyHZ8oJZIkikdOamYuv4dH8bX6C6qrYkm09mFzwESePfcJrplRzNe9z2+BY1iW3JRrKbnEpGSzcHckC3dH8phfJXo8Vo3nGnphp5UfD1FxZOfqiEvNwsfZ5r4nVtxMzWJZ2CW+33uJ+DTDLGBnWyta13GnsoM1rg5a3Oy1uFnrqZJ9Do+kkzjGH8Mq+gjKzXOQW6CxG3d5Ia2jYcTPxgW0dhD3D0pmEm6AW4FqeY4+xDk15IQSwNbU6vwW606qTkuLWm7Me61JsUfk7ld9H2cGtqrJl9vOMeHXE4TUcLt9GbwQmTl5vPvzUdYfuw7A0LYBjGgXYPH7E+DhgH9leyJvpLHjTBwvNL73eoh30uv1xuVvZGazKC3yV1A8UrLzdPwRfoX38r7lCc1pslR2rKs3k5t2Nfmh8RI6nJ1AjYTddL34MVXcuuD/1jSOJyis2HeZ7WdiOXAxgQMXExj181EcrDVUsrfC1d4aN3stley0uDlocbXX4mp366OD4fOqlWzRqGUPaFE2dkfc4N2fjxKdnImnkzUtalXmqVuHh1Px752LvJHGd39d4JdDV8jKNWxjV7WSLf2e8qd70yrYp0TeXp7l7CGIPgG6HPOGKvkZZgtb2UFmImQkmn7MNmwLR3aK4Ui6fPu5GlvwCb49waRKM9TOVfACvIB2wPjsXGKTs6jmavfA760b3KYWG09EExGbysfrTzHzlaBC68WnZdN/2UEOXUpAo1L4rFsjXm5asgkviqLQvr4n3+y8wKaT0SVKEq8kZJCcmYuVWqG2p1wREaVDkkTxyMjJ07H1XDKdM9fxqtV2dKj4o86n3LSrCUCWxpFf684gJGoBT1xZyOM315Kz7jptXl1Om8BmRCdl8vPBy/x08DJXEjJIzcolNSuXy/EZ93zt2p4O/O+t5iW62V2IomTm5PHZhn9YsueisSwmOYvVh6+y+vBVAOp4OvJUQGWeCqhMc3/XQkfAD12KZ8GuC2w+FYP+1pyShlWcGdCyBs9VzUYT/j3M/R5SY8yDsHMzrCuYf/g0AXs383oF5eVCZlKBxDHBsLVdJX/wrG9YbPou7LQa/CqXz58na42a6S83otv8Paw+cpUXGvvQOtDDpE7kjTT6Lt7PxZvpONpo+Ob1pjxZq/J9vW5ofS++2XmBHWfiyMrNs/g+6fxRxNqejmg18g+rKB2SJIpHgl6v55PNFwhIPcAHVv8DYJffMC66tjCtqKgIq/4OsQ6BhJ6diPXVfbDgGXjlf3hVacKQtgEMblOL5IxcbqZlGWdxxqdlczMtm4SCn9+a6RmXmsXZmFR6LdzPTwNCcLYr2f1EQhR0/EoSw386wvm4NABef6Iao56tw6nryfwVcYPd5+I4eS2ZMzEpnIlJYeHuSKzUCk2qVeLpgMo8FeBOdFImC3ad53BUorHdNoEeDHiqOs3zDqMcGgFrN4PeMKpoGOULupUQNjF8dKlu+Zp9ao0hkbxXMllBBVerRL+n/Pn2r0jeX3OczSNaGu8TPHAxnv7LDpKYnkPVSrYs6ftYqdzLHFTVBQ9Ha2JTsthz/iat63jc+0kF3L4fUS41i9IjSaJ4JMzZeo5TJ46wWvslakXPcY8XOeLzapH1z7u1YlHNOQy48SnqhAuwqAO8MAuCXkNRFJztrHC2s6KG+71f+9LNNF7+Oox/olPou2Q//3urudzPKEosN0/H/B3nmb01glydHndHa6a/3MiYNLSoVZkWtSoDgcSnZbPn/A12R9zgr4gbXE3MYF9kPPsi4/l881ljm1q1iq7BVXi7qQM1Lq+G35ZCUtTtF/V/Bpq9CXU6GraWE4x8tg5bTsVw8WY6Uzf8w6ddG/Jr+FVG/3yM7Dwdjas6813vx3B3tC6V11OpFJ6t58nyfVFsPhltcZKYP7NZJq2I0iR/ySqq5Gtg737PyzIPXG42ZMRD+s07jjvKMpPArdatHQ8eA88G5jMdS8kvh66w5M+DrNUaZjJfcQpmW8337jn6cdOmOsnd11Bpxzg48wesHQjXjkDop3f/uuv1hv4mXoLEKKonXeGX52rzwjoNh6MSefv7Q3zXu5ksqyMsFnkjjZErwzlya+SvU0NvpnRpUOTkCVd7Lc838uH5Rj7o9Xou3kxnd0Qcu8/dYM/5m6gUhdeb+/KW7zUqnZoJ3/8GuluzTmxcIPh1aNrHor2B/y1stWo+69aIHgv2smJfFNm5On45dAWA0PqezHolGFtt6f6Mh9b3Yvm+KLacimFKF71FC//njyQ2qCIjiaL0SJJYUf3vZbhxFtwDDffwFDwcPEu0ZVOJRf4Fmz+E+Au39xYtjquH4NhPhs+t7Az3Mvk+ZtgVwfdxsL+/e3jAcEP/+FWHWaydTXVVLAlWXvwWOB2dqnjJtd7aCV5ZDrv+D3Z8CvsXGG7W7zzH0NfEqALH5duf56SZtFMd+KPZWJ7dF8RfETcY+dNR5rwaLLu7iGLR6/Us3xfFJ7+fJiMnD0cbDR+/2IAXg3yKPVNWURT8K9vjX9meN0L80KXEopz4BeXQB7D39qgiVR+DZv2gfhewKqV9ih9RT9Rw4/UnqvG/vVHGBPGtp/wZ17FumfxsP1HDDUcbDTdSszkSlUAzP9diPS82xbCIuaJAoJckiaL0SJJYEenyIOW6YVZhzHHDUZCd262EscHtxNE9sMS/8NOzc7mZarjPLj4tixu3dlRISknhiQvzeCZ+pUn9PBQS9Y7E6x2Jx5EEvSPxegcSMJQl6B1Jx4ZAVRTN1OcIVp3DPicdLu02HPkq+RuSxaqPGT56NbIo+T19PZl3/neQCapFPKE6jc7KgZ/8PibTysWyL4BKBa3eA+9GsHoARO2Buc3u/TxHb3D2NXzdI3dS9eBn/FmvD61PPMvvx6/jaKNh6ksN73u5EvFoi03OZMyqY+w4Y1hA+smabnz+n8b4uFjw85yVCtfD4ephwz9n1w6jSixwOdnKHhp1N1xS9m5Uuh14xI19ri67zt7gSkI6kzrXp1eIX5m9llajom2gB2vDr7HpZHSxk8T8UUT/yvbYW8ufdVF65LupIlKpYcwFw4hVzMlbxwnDx/jzhsu5kbsMRz5FZRihazUWarS6a7KVnp3L+6uPc+BiAjfTssjM0ZnVqa9E8oXVV9RWGWZQrshtw8K857ihdyYZO/Tcnj2nVinYWamx1aqx06qx1WrQqBR2xaUyKysPBR21lGs0UUXQRImgqTqCWspVSIg0HPmjjU5VoV5nqNfFkDiqip6hdz0pg76LD9A9dz2vWm1Hr6hIe24ONyJ9KfGOpXWeg/7b4Jc3Ifq4IQl0qQYuvrc+5h/VwakKWBWYybznS9j8IVXOLGGnfwytz/fgxwOXcbazYtxzdUsakXjE/XH8Oh+sOU5Ceg5ajYr3OgTS90m/uy/7kptt+H1w7fCtpPAwxP0DFLIVnlcjaNobGnYHGxlhKgkHaw2/D32K1KxcvJ3LfuQ1tL7XrSQxhvc71i3WP5mn8i81y/2IopRJklhRKQpUqm44AjveLs/JMPxBKJg8Rp8w3Cd4eS983wX8noa2Ew2Xdgsx4deTrA2/ZlJmrVFR2cGaynZqeuWtpkvS96jJI93KjUONJ+NR41k+tdFgp9UYk0FDQqhGq1YV+ossT6fn4s00TlxN4uS1Wpy42pgNV5NIzszFiVSCVOeNiWMTVQQOyVdg71ew9yviVW4cdWzJ2cptSXALxsnOBmdbK5xtrXCyseLTP04TmLqXD7TLDV+u9lPI8WsNkefu7+teOQDe+Qvyciy7H/TJIeDgBWsH4n1lAzt9btLu6gC+2XkBZ1sr/tuq1v3FJR6svBzDPwoxJwwzfu0rGw67Wx9LcK+wTqfnwo00DkclcCQqgUOXEjgbY1hPsL6PE7NeCSLAw8GwVExqDKREF/gYDamxcPOcIa68bPMXcKpimJHs08Tw0Tuo1Leu+7dytLEq8S4olnqmjjvWGhVR8en8E51CXe97J/f5y9/IzGZR2iRJfNhY3VqI1if4dpleD0m3EqwD38HFv2BhO8NMxTYfGi5H37L68BV+OXQFlQKzewQT5OuCq70WO60aJf4CrHkbrhwwVK7bGbvnZ/F0CZexUKsUaro7UNPdgReDqtwKVc+VhAxOXE3ixLXGHLmazPdXk0hNS6Wl6hjPqffTTnUIV91NWietoXXSGmLPubAh7zF+0zVnvy4QHSpqKVeYaz3XsJ1X8BvwxH8hIaHEX1bz4EvwB6HRfwwJxE+v43lzPzsqJ/DcjeFM33gGZ1srejavXnrxidKVEg2X9xu+968cMExgys0sur6N862E0f1W8uhm+Ny2kuEfPF0eWTk5XE9I51piGtGJacQmpZOdm4saHdXQ44cOW6scmrvnEmCXhurHGEiJgdx7r8uJjcvtZWryk0JHr1L7cojyY6fV8HRAZf48Hcumk9HFShJPXJXt+ETZkCTxUaAohkuiHaYakqWd0yB8uWHG7pkN0PBlaDWO8zpPPlx7AoBhbWvfXtVfrzckl1smQE46WDtBx/+DRq+U+gQZRVHwdbXD19WO527ti6rX64lPyyYh/VmSMnI4nJqKddQu3C9vxDdmGx55ifTWbKE3W0hUubBbE0KzvCM45KVD9RbQaeaDnchzNzVbQ98/4H8v45YawRbnKXRNHsWHa8HJxqpEOymIUpabZRiNK5gUFtwR5JZMtSPnrQJQ6XU46RJx0iVhn5eECt2thaKTDLd/FMEa8Lt1GBX2Gzf+1lGQ1hEcPQ2j046ehslqDp6Gn3OfYMP9vBXle16Uuvb1vfjzdCybT8YwvF3tu9ZNzswhKt6w77aMJIrSJknio8bFF16cCy2GwfZP4OQaOP4z+pNr+EfTDsfs52lcw7BgNADJ1+HXQXB+q+Gxf0t48StDOw+Ioii4OVjj5pC/3lglqN8T6Gm4/ypyJ5xcC/+sxyUzkeezN9zqa3Xo/n3FW9fNuzH02wz/64Zz/HnW203m9fSRjPhJwcFGY/H6Zw+1vFzDyJh1OW4TpsuDa+Fw7k84v81wL98dl2v1iopY2xqE6wx7Bx/Mq0Wk3svk3lsABR0upOKqpFCZZFyVZNyUZNxufe6ipKEHdKjQocJaa4Wbgw1uDra4OdlQycEWtUpjuIdYpQK19lYC6GGaEGpLfGeteAS0q+uJSoFT15O5HJ+Or6tdkXXz70es4mJ7132mhSgJSRIfVZUD4D9L4KkRsG0KSsRmOmVvpJ3NVnK930KdEQCRO2D9SMPWWRobaDcJHn/7rhNGHjiNFgKeNRx5swwJ46lfIT4SOs2ouDs6uPobEsXl/8Hu2mF+tJnKO1lDGPg/he/7NeexImYt6vV60rLziEvJ4kZqFjdSsohPz8bBWoO7ozUejjZ4OlnjYK25fR9obrZhRCv2tOF+1fR4aPKGIVktT6lx8L+utycBudcB97q3PgaCR6Dh8mxZSIkx/OOTnxhmmN6KkK2tRKRNPcKya7I52ZejuhqkZdyelODtbENnf1eaVa+EtZWarFwdWTl5ZOXqyMzJI7PA58k5OuJy88jM0aHT66nr7USTai40qVbJon2Vhcjnaq/lcX9X9l6IZ9PJaN56ukaRdfMX0a4no4iiDEiS+KjzbsyGRnNYeGIFo61W0lz1D9aH5kP4IsjLulUnCF5aYPjjXZGpraBWO8PxMLCvDH3Ww8reaM9t4TvtTMbl9OPNJSqGtQ0gOSOHuNQs4lKyDQnhraOw2eYAGnKprsRQW7lCPc1VGlhdo5ZyBZ/cq6jJM6mrD1+B8sqy8vtapUTD0s5w48ytx9cNx4UdpvUcPA0J462kUedWB6WSL4qNs+GSa3H/YcnLgcv7DEnhuT8NiWkBemtHLjk3Z21KIGsSa3Ap0xOSb1+u9a9sz+N+rjzubziqVrKVpYtEuQqt78XeC/FsPhlz1yRRZjaLsiRJ4iPucnw6Y1YdI0UfyLYnFtM84Bps/Qiij4GihqdHwTNjKt7OLo8KrT28+gP8NgxV+HKmWX2LR04CU37vCoAtWTiThouSRlUllfqk4axOxUOdjpd1Ju6aDNyUFDyzo/DKvYoVubfbLpAXpuhtOaevwlldVfxU0TTP+Qf9ildQusw3rI/3ICVdhaUvGEY3naoY+p+Xc3ukM+4fiDtjuA8wNcZwRO4E4M6UUK91RLFxMlyutr71seBjrYNhBvKFnZCdYvpk7yCSqj7D2uS6zPrHiYSk26cCvRyNCeHjfq4y4icqnGfrefLRb6c4eCmeG6lZVDbejmNK9mwWZUmSxArqxNUkrDUqAjxLfi9Xdq6OwT8cISUzl+BqLrwbGgjqelCzDVzYbpgNWWDmsygjait4cZ7hkutfnzPK6hcGWG/BVp+ORp9T9PNybh0FaR3QVa5NunMA8fY1ua7146KqGpHZLsSmZhGXksXhyFg+1X/Fi+yB1f0NS6c8Obgse3hbYpQhQUy4CM7VoM9vUMmPPJ2eSG0djpHE8ewkjqckERkfQ9XcKAJUVwlQrhCgXKW26gruJGKtGJJhJTvFPPkrip0b1GyLvlZbDmuCWXA4hS27Y9DdWj6wRmV7+rTw44VGPnLvlqjwqlayo0EVJ05cTWbr6RheeayaWZ3MnDzOxd1aRkm24xNlQJLECig7V8eIn8K5eDONAS1rMKRNADZWlu8R+vnmMxy9nIiTjYYvXw3GSn1rnEalglptSzlqcVeKAm3HGxLzP0bjqCswrKXSGO7Ns3ExfLTN/1igzLWG4R4+p6qoVCocAAegGtD8jpfa9k8Mby9TuKF3pp9mA2z+wDBa1+6jsr3fNP6C4RJz0mWo5E/uG7/y/SkdG46HcfJaEmnZeXc8QUumtg7WPo/jXNUZr6rOZFVxZm9CBr8evMDe05Foc1NxIAMnJYNm3hqe9tXSyF2FdW4aZCUZ1hR0qgq12pLl0ZDfjsWwaEckp67fXi/z6YDKvNnCn2dqu999kWohKpjQel6cuJrMppOFJ4n/RKeQp9PjZq/FS0bDRRmQJLECSs/OpbqbHRGxqczbfp7fjl5nSpcGtKztXuw2tp+JZcGuCwBMf7kxVSsVPTtOPECP94fATpB243ZCqHUo1eVM2gR6MvOVJgz9EWL1Loyz+gH2zDGMKL44t2xuLbgRYUgQU66BWy3+CV3Bu8ujjOu3Adhaqanv40TDqs40qupMwyrO+Fd2MNsDt6a7A8/UdiclswkbTkSz+vAV9lyIZ89VmHPV0M5zDbx4qUlVQmq6EZ+WzfJ9l/jf4h3cSDXMWraxUvFSk6r0fdLvvkbjhShPoQ28mLHlLLsjbpCalYvDHVvu5S+iXc/HSe6hFWVCksQKyMVOy7e9mrHpZAyT1p0kKj6dXov207mxDx8+XxcPx7v/xxidlMmolUcB6B1SnQ4NZJHdCsXJx3CUoRca+5Calcu41YYRxf/Tfovq2I+GLR27Ly3dJVZi/4FlnSE1Bl3lOsz1ncnsJefJ0+lxstEwvF1tngqoTE1384TwbhxtrOjezJfuzXy5HJ/O2iNXWX3kKpE30lh963MPR2sS03PIzjNM9vF2tuGNkOq8+lg1uaQsHnoBHg74V7Yn8kYaO8/E0amRt8n52/cjyqQVUTYkSaygFEWhQwMvngqozIzNZ1i65yLrjl5j+5lY3usQyGuPVyv00lmeTs+wH48Qn5ZNPW8nxnWUfYP/rV59vBqpmbl88gfEZzvyrc2XaM5tMYz4vbaydJYPij4By16E9BukuQTSI20cx8MMoxudGnozsXO9e/5TUxy+rnYMaWtY3/PI5URWH77Cb0evE5timKHfpJoLfVv406GB1+3bKoR4yCmKQvt6nnyz6wKbTkabJ4lXZTs+UbYkSazgHKw1THyhPi8FV+X9Ncc5fjWJD9eeYPXhK3z6UkMCvUx/OczZGsG+yHjstWrmvhZconsZxaOjf8sapGTmMGcb/CdjHD86zMT66kFYFApvrAYX8/uciu1auGGv8IwELtvU5oXoESRihbezDR+/2IB29TxLqxtGiqLQpFolmlSrxPjn67H3Qjxu9loaVJGRFPFoal/fi292XWD7P7Fk5+rQagz/BOXm6fgn2jCpS77/RVmRf7kfEg2rOrN2UAsmvlAPe62aw1GJdJqzm6kbTpOebZgJuuf8DeZsiwDgk64NqeHuUJ4hiwpixLO16fOkH0f0AbyQNp4MW2+4GQEL20PMyZI1euUQ+mWdISOB4wTQKXE0SYojvUOqs3lEyzJJEO9krVHzTG13+QMpHmnBvi54OFqTkpXLnvM3jOXn49LIytXhYK2h+l12ZBHifpRrkpiXl8f48ePx9/fH1taWmjVr8vHHH6PX64119Ho9EyZMwNvbG1tbW9q1a0dERIRJO/Hx8fTs2RMnJydcXFzo168fqampJnWOHTvG008/jY2NDb6+vkyfPv2B9LE0qVUKfVv48+eoZ+hQ34s8nZ5vdl7g2Zm7+DX8KsN/DEevh+7NqtIluEp5hysqCEVRmPB8PV5uWpWzOh/aJ39ImnOAYXHrRc/BpT2WNRi1F92yziiZSRzQ1ebVzPfw9PDkl3ee5KMXG+BoI2tuClFaVCqFZ2/907XpZIyxPH+nlbrejjJrX5SZcr3cPG3aNObPn8/SpUupX78+Bw8epG/fvjg7OzN06FAApk+fzpw5c1i6dCn+/v6MHz+e0NBQTp06hY2N4V6nnj17cv36dbZs2UJOTg59+/ZlwIABrFixAoDk5GTat29Pu3bt+Prrrzl+/DhvvvkmLi4uDBgwoNz6X1LezrZ8/UZT/jwVw8R1J7mamMGwH8MBqOXhwKTOsvahMKVSKXz2UkPSsnLZcALaxI/lT5/5OMYcNKxraO8BGmvD9owaa7CyNX1866NOpSXvyHKs8jIIy6vHQN0YBjzbgHeeqWm8DCaEKF2h9b1Yvi+KLadi+KRLA1QqRSatiAeiXJPEPXv28OKLL9KpUycA/Pz8+OGHH9i/fz9gGEWcNWsWH374IS+++CIAy5Ytw9PTk7Vr19KjRw9Onz7Nxo0bOXDgAM2aNQPgyy+/pGPHjnz++ef4+PiwfPlysrOzWbRoEVqtlvr16xMeHs7MmTMfyiQxX7t6noTUdGP21ggW7o5Eq1Yx77Um2GnlVlNhTqNWMatHEGnLDrHrbBytY4azze9/OF3caFi6phhUt45deQ35xmcyv3R7nFoecluDEGXpiRpuONpouJGaxZHLCTSt7mpc/kYmrYiyVK7ZxJNPPsmCBQs4e/YstWvX5ujRo+zevZuZM2cCEBkZSXR0NO3a3d5/1tnZmebNmxMWFkaPHj0ICwvDxcXFmCACtGvXDpVKxb59++jatSthYWG0bNkSrfb2khihoaFMmzaNhIQEKlWqZBJXVlYWWVlZxscpKcXc8aEc2FtreL9jXXqFVAeQ9RDFXVlr1HzzelN6LdrHgYsJtI7qx+qeH1LNPpebSSlERt/kUkw8V+MSuX4zkbycTKzJwZpsrMnBRskmXeNK9ecG8v0TAXKZS4gHQKtR0SbQg1/Dr7HpZAzBvpWMezbLSKIoS+WaJI4dO5bk5GQCAwNRq9Xk5eXxySef0LNnTwCio6MB8PQ0vQne09PTeC46OhoPDw+T8xqNBldXV5M6/v7+Zm3kn7szSZw6dSofffRRKfXywZDkUBSXrVbNwj6P8dq3ezlxNZkuP1xHo1YRl5IFaAGvWwdo1SrqejvSqKoLdao607iqCzXd7dHIMjNCPFCh9b1uJYnR9GxejZSsXLRqFQGeMpIvyk65JokrV65k+fLlrFixwngJePjw4fj4+NC7d+9yi2vcuHGMHDnS+Pjq1avUq1ev3OIRorQ52VixtO/jdP8mjPNxaYBhYlRtT0caVXGmka8zjaq4UMfLUe41FKICeKa2O1qNiks301l9+CoAdbwcZV1QUabKNUkcPXo0Y8eOpUePHgA0bNiQS5cuMXXqVHr37o2Xl2E0IyYmBm/v24uIxsTEEBQUBICXlxexsbEm7ebm5hIfH298vpeXFzExMSZ18h/n1ynI2toaa2tr4+Pk5GSzOkI87NwcrPn5nSf583QMNd3tqeftjK1W1tUUoiKyt9bQMqAyf56OZdHuSEDuRxRlr1z/BUlPT0elMg1BrVaj0xm22PL398fLy4utW7cazycnJ7Nv3z5CQkIACAkJITExkUOHDhnrbNu2DZ1OR/PmzY11du3aRU5OjrHOli1bqFOnjtmlZiH+TVzttXRv5kvT6q6SIApRwbWvZxjUSMkyrI1bX9YIFWWsXJPEF154gU8++YTff/+dixcvsmbNGmbOnEnXrl0Bw/puw4cPZ8qUKaxbt47jx4/Tq1cvfHx86NKlCwB169alQ4cO9O/fn/379/P3338zePBgevTogY+PYX/c1157Da1WS79+/Th58iQ//fQTs2fPNrmkLIQQQlRkbet6UHCumIwkirJWrpebv/zyS8aPH89///tfYmNj8fHx4e2332bChAnGOmPGjCEtLY0BAwaQmJjIU089xcaNG41rJAIsX76cwYMH07ZtW1QqFd26dWPOnDnG887OzmzevJlBgwbRtGlTKleuzIQJEx7q5W+EEEL8u7g5WPOYnyv7IuNRKVDXS5JEUbYUfcHtTUShrly5gq+vL5cvX6Zq1arlHY4oQnx8PF9tP4e9k0ux6qclJ/Lf1rVwdXUt28CEEKKULNodyeT1pwjwcGDLyGfKO5wKT/5+3x9ZdVkIIYR4SLzymC//RCcTWt980qUQpU2SRCGEEOIhYW+tYfrLjcs7DPEvIQssCSGEEEIIM5IkCiGEEEIIM5IkCiGEEEIIM5IkCiGEEEIIM5IkCiGEEEIIM5IkCiGEEEIIM5IkCiGEEEIAeXl5jB8/Hn9/f2xtbalZsyYff/wxBfcd0ev1TJgwAW9vb2xtbWnXrh0RERHG81lZWbzxxhs4OTlRu3Zt/vzzT5PX+L//+z+GDBnywPp0P2SdRCGEEEIIYNq0acyfP5+lS5dSv359Dh48SN++fXF2dmbo0KEATJ8+nTlz5rB06VL8/f0ZP348oaGhnDp1ChsbGxYsWMChQ4cICwtjw4YNvPbaa8TExKAoCpGRkXz77bccPHiwnHtaPDKSKIQQQggB7NmzhxdffJFOnTrh5+fHyy+/TPv27dm/fz9gGEWcNWsWH374IS+++CKNGjVi2bJlXLt2jbVr1wJw+vRpOnfuTP369Rk0aBBxcXHcuHEDgIEDBzJt2jScnB6OfbdlJNECubm55OTkAKBSqVCr1eTl5aHT6Yx18stzc3NNhqfVajUqlarI8vx282k0GuNrFqfcysoKnU5HXl6esUxRFDQaTZHlRcX+sPYJQEGPos8r0IqCXlGh6HVAgcsFGOrn5eWZxFnR+vQovk/SJ+mT9En69CD7BJCSkkJycrLxvLW1NdbW1tzpySefZMGCBZw9e5batWtz9OhRdu/ezcyZMwGIjIwkOjqadu3aGZ/j7OxM8+bNCQsLo0ePHjRu3Jjvv/+ejIwMNm3ahLe3N5UrV2b58uXY2NjQtWtXs9etqCRJtEBYWBh2dnYAVKtWjeDgYI4dO0ZUVJSxTp06dQgMDGT//v3ExcUZy4OCgqhevTq7du0iJSXFWB4SEoKHhwebN282+cFq3bo1tra2/PHHHyYxdOzYkYyMDLZv324s02g0dOrUiRs3bhAWFmYsd3R0pE2bNly+fJnw8HBjubu7O08++SQRERGcOXPGWP6w9ykwMBB3VSqeGdHG8mS1MzesfXDLjsYpL8lYnqCpTBpWnDp1isTExArbp0fxfZI+SZ+kT9KnB9Wn/Lbr1atnEuvEiROZNGkSdxo7dizJyckEBgYak9JPPvmEnj17AhAdbfj74unpafI8T09P47k333yTY8eOUa9ePSpXrszKlStJSEhgwoQJ7Nixgw8//JAff/yRmjVrsmjRIqpUqWIWR0Wh6Aum3qJQV65cwdfXl8jISOObKf+pVbw+JScnM397BPaOBYfxix5JTEtJ5u2W/ri4uFTYPj2K75P0SfokfZI+Pag+Xbx4EX9/f06dOmWSjBU1kvjjjz8yevRo/u///o/69esTHh7O8OHDmTlzJr1792bPnj20aNGCa9eu4e3tbXxe9+7dURSFn376yaxNgL59+xIUFIS/vz/vv/8++/btY/r06Zw4cYJVq1YV+pyKQEYSLaDRaLCysjIpU6vVqNXqQusW1UZh7my3JOUqlQqVyvw206LKi4r9Ye6THgW9Yh67Xin89lu1Wl3o61akPj2K75P0SfpUVIyWlkufpE9w7z45OjoW6z7A0aNHM3bsWHr06AFAw4YNuXTpElOnTqV37954eXkBEBMTY5IkxsTEEBQUVGib27dv5+TJk3z33XeMHj2ajh07Ym9vT/fu3Zk7d+49YypPMnFFCCGEEAJIT083S1rVarVx9NLf3x8vLy+2bt1qPJ+cnMy+ffsICQkxay8zM5NBgwbxzTffGEdC80dmc3JyTEZRKyJJEoUQQgghgBdeeIFPPvmE33//nYsXL7JmzRpmzpxpnGyiKArDhw9nypQprFu3juPHj9OrVy98fHzo0qWLWXsff/wxHTt2JDg4GIAWLVqwevVqjh07xty5c2nRosWD7J7F5HKzEEIIIQTw5ZdfMn78eP773/8SGxuLj48Pb7/9NhMmTDDWGTNmDGlpaQwYMIDExESeeuopNm7ciI2NjUlbJ06cYOXKlSYTc15++WV27NjB008/TZ06dVixYsWD6lqJyMSVYsifuHL58mWqVq1a3uGIIsTHx/PV9nPYO7kUq35aciL/bV0LV1fXsg1MCCFEuZC/3/dHLjcLIYQQQggzkiQKIYQQQggzkiQKIYQQQggzkiQKIYQQQggzkiQKIYQQQggzkiQKIYQQQggzkiQKIYQQQggzkiQKIYQQQggzkiQKIYQQQggzkiQKIYQQQggzsnez+NfS6XQkJCRY9BwXFxdUKvnfSgghxKNPkkTxr5WZlsL8rTdxcXMvdv2RzwfLXs9CCCH+FSRJFP9qNvaO2Du5lHcYQgghRIUj182EEEIIIYQZSRKFEEIIIYQZSRKFEEIIIYQZSRKFEEIIIYQZSRKFEEIIIYQZSRKFEEIIIYQZSRKFEEIIIYQZSRKFEEIIIYQZSRKFEEIIIYQZSRKFEEIIIYQZSRKFEEIIIYQZSRKFEEIIIYQZSRKFEEIIIYQZSRKFEEIIIYQZSRKFEEIIIYSZ+04Sk5OTWbt2LadPny6NeIQQQgghRAVgcZLYvXt35s6dC0BGRgbNmjWje/fuNGrUiFWrVpV6gEIIIYQQ4sGzOEnctWsXTz/9NABr1qxBr9eTmJjInDlzmDJlSqkHKIQQQgghHjyLk8SkpCRcXV0B2LhxI926dcPOzo5OnToRERFR6gEKIYQQQogHz+Ik0dfXl7CwMNLS0ti4cSPt27cHICEhARsbm1IPUAghhBBCPHgaS58wfPhwevbsiYODA9WrV6dVq1aA4TJ0w4YNSzs+IYQQQghRDixOEv/73//y+OOPc/nyZZ599llUKsNgZI0aNeSeRCGEEEKIR4TFSSJAs2bNaNasmUlZp06dSiUgISoqnU5HQkKCRc9xcXEx/iMlhBBCPEwsThLz8vJYsmQJW7duJTY2Fp1OZ3J+27ZtpRacEBVJZloK87fexMXNvdj1Rz4fbJzoJYQQQjxMLE4Shw0bxpIlS+jUqRMNGjRAUZSyiEuICsnG3hF7J5fyDkMIIYQocxYniT/++CMrV66kY8eOZRGPEEIIIYSoACy+WUqr1VKrVq2yiEUIIYQQQlQQFieJo0aNYvbs2ej1+rKIRwghhBBCVAAWX27evXs327dvZ8OGDdSvXx8rKyuT86tXry614IQQQgghRPmwOEl0cXGha9euZRGLEEIIIYSoICxOEhcvXlwWcQghhBBCiAqkRItpA8TFxXHmzBkA6tSpg7t78daOE0IIIYQQFZ/FE1fS0tJ488038fb2pmXLlrRs2RIfHx/69etHenp6WcQohBBCCCEeMIuTxJEjR7Jz505+++03EhMTSUxM5Ndff2Xnzp2MGjWqLGIUQgghhBAPmMWXm1etWsUvv/xCq1atjGUdO3bE1taW7t27M3/+/NKMTwghhBBClAOLRxLT09Px9PQ0K/fw8CjR5earV6/y+uuv4+bmhq2tLQ0bNuTgwYPG83q9ngkTJuDt7Y2trS3t2rUjIiLCpI34+Hh69uyJk5MTLi4u9OvXj9TUVJM6x44d4+mnn8bGxgZfX1+mT59ucaxCCCGEEP8WFieJISEhTJw4kczMTGNZRkYGH330ESEhIRa1lZCQQIsWLbCysmLDhg2cOnWKGTNmUKlSJWOd6dOnM2fOHL7++mv27duHvb09oaGhJq/fs2dPTp48yZYtW1i/fj27du1iwIABxvPJycm0b9+e6tWrc+jQIf7v//6PSZMmsWDBAku7L4QQQgjxr2Dx5ebZs2cTGhpK1apVady4MQBHjx7FxsaGTZs2WdTWtGnT8PX1NVlWx9/f3/i5Xq9n1qxZfPjhh7z44osALFu2DE9PT9auXUuPHj04ffo0Gzdu5MCBAzRr1gyAL7/8ko4dO/L555/j4+PD8uXLyc7OZtGiRWi1WurXr094eDgzZ840SSaFEEIIIYSBxSOJDRo0ICIigqlTpxIUFERQUBCfffYZERER1K9f36K21q1bR7NmzfjPf/6Dh4cHwcHBfPvtt8bzkZGRREdH065dO2OZs7MzzZs3JywsDICwsDBcXFyMCSJAu3btUKlU7Nu3z1inZcuWaLVaY53Q0FDOnDlDQkKCpV8CIYQQQohHXonWSbSzs6N///73/eIXLlxg/vz5jBw5kvfff58DBw4wdOhQtFotvXv3Jjo6GsDsHkhPT0/juejoaDw8PEzOazQaXF1dTeoUHKEs2GZ0dLTJ5W2ArKwssrKyjI9TUlLuu69CCCGEEA+TYiWJ69at47nnnsPKyop169bdtW7nzp2L/eI6nY5mzZrx6aefAhAcHMyJEyf4+uuv6d27d7HbKW1Tp07lo48+KrfXF0IIIYQob8VKErt06WIcsevSpUuR9RRFIS8vr9gv7u3tTb169UzK6taty6pVqwDw8vICICYmBm9vb2OdmJgYgoKCjHViY2NN2sjNzSU+Pt74fC8vL2JiYkzq5D/Or1PQuHHjGDlypPHx1atXzeIUQgghhHiUFeueRJ1OZ7ykq9PpijwsSRABWrRoYdzaL9/Zs2epXr06YJjE4uXlxdatW43nk5OT2bdvn3EmdUhICImJiRw6dMhYZ9u2beh0Opo3b26ss2vXLnJycox1tmzZQp06dcwuNQNYW1vj5ORkPBwdHS3qlxBCCCHEw87iiSvLli0zuV8vX3Z2NsuWLbOorREjRrB3714+/fRTzp07x4oVK1iwYAGDBg0CDCOTw4cPZ8qUKaxbt47jx4/Tq1cvfHx8jCOadevWpUOHDvTv35/9+/fz999/M3jwYHr06IGPjw8Ar732Glqtln79+nHy5El++uknZs+ebTJaKIQQQgghbrM4Sezbty9JSUlm5SkpKfTt29eith577DHWrFnDDz/8QIMGDfj444+ZNWsWPXv2NNYZM2YMQ4YMYcCAATz22GOkpqayceNGbGxsjHWWL19OYGAgbdu2pWPHjjz11FMmayA6OzuzefNmIiMjadq0KaNGjWLChAmy/I0QQgghRBEsnt2s1+tRFMWs/MqVKzg7O1scwPPPP8/zzz9f5HlFUZg8eTKTJ08uso6rqysrVqy46+s0atSIv/76y+L4hBBCCCH+jYqdJAYHB6MoCoqi0LZtWzSa20/Ny8sjMjKSDh06lEmQQgghhBDiwSp2kph/D2B4eDihoaE4ODgYz2m1Wvz8/OjWrVupByiEEEIIIR68YieJEydOBMDPz48ePXpgbW1dZkEJIYQQQojyZfHElXr16hEeHm5Wvm/fPg4ePFgaMQkhhBBCiHJmcZI4aNAgLl++bFZ+9epV49I1QgghhBAPo6tXr/L666/j5uaGra0tDRs2NBkE0+v1TJgwAW9vb2xtbWnXrh0RERHG81lZWbzxxhs4OTlRu3Zt/vzzT5P2/+///o8hQ4Y8sP7cD4uTxFOnTtGkSROz8uDgYE6dOlUqQQkhhBBCPGgJCQm0aNECKysrNmzYwKlTp5gxY4bJxhvTp09nzpw5fP311+zbtw97e3tCQ0PJzMwEYMGCBRw6dIiwsDAGDBjAa6+9hl6vByAyMpJvv/2WTz75pFz6ZymLl8CxtrYmJiaGGjVqmJRfv37dZMazEEIIIcTDZNq0afj6+rJ48WJjmb+/v/FzvV7PrFmz+PDDD3nxxRcBwyYjnp6erF27lh49enD69Gk6d+5M/fr1qVGjBqNHj+bGjRu4u7szcOBApk2bhpOT0wPvW0lYnNW1b9+ecePG8euvvxrXRUxMTOT999/n2WefLfUAK5Lc3Fzj1n4qlQq1Wk1eXh46nc5YJ788NzfX+J8DgFqtRqVSFVlecMtAwJhw5+bmFqvcysrKbGtERVHQaDRFlhcV+8PaJwAFPYq+4PaQCnpFhaLXAbdj1GOor7qjvh4VKEoh9VVF1wcUbn+9bpfrTb5nStKnR/F9kj5Jn6RP0qcH2ScwbPiRnJxsPG9tbV3oBNx169YRGhrKf/7zH3bu3EmVKlX473//S//+/QHDSGB0dDTt2rUzPsfZ2ZnmzZsTFhZGjx49aNy4Md9//z0ZGRls2rQJb29vKleuzPLly7GxsaFr165mr1tRWZwkfv7557Rs2ZLq1asTHBwMGJbF8fT05Pvvvy/1ACuSsLAw7OzsAKhWrRrBwcEcO3aMqKgoY506deoQGBjI/v37iYuLM5YHBQVRvXp1du3aRUpKirE8JCQEDw8PNm/ebPKD1bp1a2xtbfnjjz9MYujYsSMZGRls377dWKbRaOjUqRM3btwgLCzMWO7o6EibNm24fPmyyWQjd3d3nnzySSIiIkz2zn7Y+xQYGIi7KhXPjGhjebLamRvWPrhlR+OUd3unoARNZeKAWjapOGckGsvjtF6kaCpRJTMSrT7bWH7d2tfQZ/tE1AXqX7apQa6iwT/jrEmfIm1rY02uSezyPkmfpE/SJ+nTg+1Tftv16tUziXXixIlMmjSJO124cIH58+czcuRI3n//fQ4cOMDQoUPRarX07t2b6GjD3xdPT0+T53l6ehrPvfnmmxw7dox69epRuXJlVq5cSUJCAhMmTGDHjh18+OGH/Pjjj9SsWZNFixZRpUoVszgqCkVfMPUuprS0NJYvX87Ro0extbWlUaNGvPrqq1hZWZVFjOXuypUr+Pr6EhkZaXwz5T+1iten5ORk5m+PwN6x4DB+0SOJcdcuo7HS4uruUaC86JHEuGtRhden8JHEtJREBjztb3Ivi7xP0ifpk/RJ+vTg+nTx4kX8/f05deqUSTJW1EiiVqulWbNm7Nmzx1g2dOhQDhw4QFhYGHv27KFFixZcu3YNb29vY53u3bujKAo//fSTWZtg2NI4KCgIf39/3n//ffbt28f06dM5ceIEq1atKvQ5FUGJbiK0t7f/V+57rNFozBJhtVqNWq0utG5RbRSmqATbknKVSoVKZT4XqajyomJ/mPukR0GvmMeuVwqfo6UrrfqYl4FS6PeMvE/SJ5A+FRWjpeXSJ+kT3LtPjo6OxboP0Nvb22zUsW7dusZEzsvLC4CYmBiTJDEmJoagoKBC29y+fTsnT57ku+++Y/To0XTs2BF7e3u6d+/O3Llz7xlTeSpWkrhu3Tqee+45rKysWLdu3V3rdu7cuVQCE0IIIYR4kFq0aGFymRvg7NmzVK9eHTBMYvHy8mLr1q3GpDA5OZl9+/YxcOBAs/YyMzMZNGgQy5cvN46E5o945uTkmIyiVkTFShK7dOlCdHQ0Hh4exu35CqMoSoXvsBBCCCFEYUaMGMGTTz7Jp59+Svfu3dm/fz8LFixgwYIFgCHPGT58OFOmTCEgIAB/f3/Gjx+Pj49PofnRxx9/TMeOHY1zOFq0aMHo0aPp27cvc+fOpUWLFg+yexYrVpJY8Pp/wc+FEEIIIR4Vjz32GGvWrGHcuHFMnjwZf39/Zs2aRc+ePY11xowZQ1paGgMGDCAxMZGnnnqKjRs3YmNjY9LWiRMnWLlypcnEnJdffpkdO3bw9NNPU6dOHVasWPGgulYiJZq48m+TP3Hl8uXLVK1atbzDEUWIj4/nq+3nsHdyKVb9uKuXUFlZ4+bhVSb105IT+W/rWri6uharvhBCiNIlf7/vT7FGEufMmVPsBocOHVriYIQQQgghRMVQrCTxiy++MHkcFxdHeno6Li4ugGExbTs7Ozw8PCRJFEIIIYQoB3l5eSxZsoStW7cSGxtrdovgtm3bLGqvWEliZGSk8fMVK1bw1VdfsXDhQurUqQPAmTNn6N+/P2+//bZFLy6EEEIIIUrHsGHDWLJkCZ06daJBgwbG3chKyuJ1EsePH88vv/xiTBDBsOL5F198wcsvv2xyc6cQQgghhHgwfvzxR1auXEnHjh1Lpb3CVwy+i+vXr5utpg6GIc6YmJhSCUoIIYQQQlhGq9VSq1atUmvP4iSxbdu2vP322xw+fNhYdujQIQYOHGiy4bUQQgghhHhwRo0axezZsymthWssvty8aNEievfuTbNmzYxb7+Tm5hIaGsp3331XKkEJIYQQQgjL7N69m+3bt7Nhwwbq169vtkXi6tWrLWrP4iTR3d2dP/74g7Nnz/LPP/8AEBgYSO3atS1tSgghhBBClBIXFxe6du1aau1ZnCTm8/PzQ6/XU7NmzSI31hZCCCGEEA/G4sWLS7U9i+9JTE9Pp1+/ftjZ2VG/fn2ioqIAGDJkCJ999lmpBieEEEIIISwTFxfH7t272b17N3FxcSVux+Ikcdy4cRw9epQdO3aY7FPYrl07fvrppxIHIoQQQgghSi4tLY0333wTb29vWrZsScuWLfHx8aFfv36kp6db3J7FSeLatWuZO3cuTz31lMkijfXr1+f8+fMWByCEEEIIIe7fyJEj2blzJ7/99huJiYkkJiby66+/snPnTkaNGmVxexbfTBgXF4eHh4dZeVpa2n2v7C2EEEIIIUpm1apV/PLLL7Rq1cpY1rFjR2xtbenevTvz58+3qD2LRxKbNWvG77//bnycnxh+9913hISEWNqcEEIIIYQoBenp6Xh6epqVe3h4lOhys8UjiZ9++inPPfccp06dIjc3l9mzZ3Pq1Cn27NnDzp07LQ5ACCGEEELcv5CQECZOnMiyZcuM80YyMjL46KOPSjSQZ3GS+NRTT3H06FGmTp1Kw4YN2bx5M02aNCEsLIyGDRtaHIAQQgghhLh/s2fPJjQ0lKpVq9K4cWMAjh49io2NDZs2bbK4PYuSxJycHN5++23Gjx/Pt99+a/GLCSGEEEKIstGgQQMiIiJYvny5ccOTV199lZ49e2Jra2txexYliVZWVqxatYrx48db/EJCCCGEEKJs2dnZ0b9//1Jpy+LLzV26dGHt2rWMGDGiVAIQQgghhBAls27dOp577jmsrKxYt27dXet27tzZorYtThIDAgKYPHkyf//9N02bNsXe3t7k/NChQy1tUgghhBBClECXLl2Ijo7Gw8ODLl26FFlPURTy8vIsatviJHHhwoW4uLhw6NAhDh06ZBaAJIlCCCGEEA+GTqcr9PPSYHGSGBkZWaoBCCGEEEKI+7ds2TJeeeUVrK2tTcqzs7P58ccf6dWrl0XtWbyYdkF6vR69Xn8/TQghhBBCiFLQt29fkpKSzMpTUlLo27evxe2VKElcuHAhDRo0wMbGBhsbGxo0aMB3331XkqaEKJJOpyM+Pr7YR0JCAsg/LUIIIf6l9Hp9oVskX7lyBWdnZ4vbs/hy84QJE5g5cyZDhgwxrt4dFhbGiBEjiIqKYvLkyRYHIURhEhMTmbn+CDb2jsWqnxBzFVtnN+zvXVUIIYR4ZAQHB6MoCoqi0LZtWzSa2+ldXl4ekZGRdOjQweJ2LU4S58+fz7fffsurr75qLOvcuTONGjViyJAhkiSKUmVj74i9k0ux6qanmA+xCyGEEI+6/FnN4eHhhIaG4uDgYDyn1Wrx8/OjW7duFrdrcZKYk5NDs2bNzMqbNm1Kbm6uxQEIIYQQQoiSmzhxIgB+fn688sorxn2b75fF9yS+8cYbzJ8/36x8wYIF9OzZs1SCEkIIIYQQlunduzc2NjZkZ2dz5coVoqKiTA5LWTySCIaJK5s3b+aJJ54AYN++fURFRdGrVy9GjhxprDdz5sySNC/Ev45OpyMxMdGi57i4uKBS3dcCBUIIIR4hERERvPnmm+zZs8ekPH9CS5kvpn3ixAmaNGkCwPnz5wGoXLkylStX5sSJE8Z6hc2uEUIUztJJOplpKYx8PhhXV9cyjkwIIcTDok+fPmg0GtavX4+3t/d952IWJ4nbt2+/rxcUQhTOkkk6QgghxJ3Cw8M5dOgQgYGBpdKeXKsSQgghhHgE1KtXjxs3bpRae5IkCiGEEEI8AqZNm8aYMWPYsWMHN2/eJDk52eSwVIkmrgghhBBCiIqlXbt2ALRt29ak/IFNXBFCCCGEEBVPac8bKVaS2KRJE7Zu3UqlSpWYPHky7777LnZ2dqUaiBBCCCGEKLlnnnmmVNsr1j2Jp0+fJi0tDYCPPvqI1NTUUg1CCCGEEELcv7/++ovXX3+dJ598kqtXrwLw/fffs3v3bovbKtZIYlBQEH379uWpp55Cr9fz+eefm+wLWNCECRMsDkIIIYQQQtyfVatW8cYbb9CzZ08OHz5MVlYWAElJSXz66af88ccfFrVXrCRxyZIlTJw4kfXr16MoChs2bECjMX+qoiiSJAohhBBClIMpU6bw9ddf06tXL3788UdjeYsWLZgyZYrF7RUrSaxTp47xxVQqFVu3bsXDw8PiFxNCCCGEEGXjzJkztGzZ0qzc2dnZ4q1foQTrJOp0OkkQhRBCCCEqGC8vL86dO2dWvnv3bmrUqGFxeyVaAuf8+fPMmjWL06dPA4YVvocNG0bNmjVL0pwQQgghhLhP/fv3Z9iwYSxatAhFUbh27RphYWG8++67jB8/3uL2LE4SN23aROfOnQkKCqJFixYA/P3339SvX5/ffvuNZ5991uIghBBCCCHE/Rk7diw6nY62bduSnp5Oy5Ytsba25t1332XIkCEWt2dxkjh27FhGjBjBZ599Zlb+3nvvSZIoxAOg0+lISEiw6DkuLi6oVLITpxBCPKoUReGDDz5g9OjRnDt3jtTUVOrVq1fkijT3YnGSePr0aVauXGlW/uabbzJr1qwSBSGEsExmWgrzt97Exc292PVHPh+Mq6trGUcmhBCivLz55pvMnj0bR0dH6tWrZyxPS0tjyJAhLFq0yKL2LB5WcHd3Jzw83Kw8PDxcJrQI8QDZ2Dti7+RSrMPG3rG8wxVCCFHGli5dSkZGhll5RkYGy5Yts7g9i0cS+/fvz4ABA7hw4QJPPvkkYLgncdq0aYwcOdLiAIQQQgghRMklJyej1+vR6/WkpKRgY2NjPJeXl8cff/xRooE8i5PE8ePH4+joyIwZMxg3bhwAPj4+TJo0iaFDh1ocgBBCCCGEKDkXFxcURUFRFGrXrm12XlEUPvroI4vbtThJVBSFESNGMGLECFJSUgBwdJRLWUIIIYQQ5WH79u3o9XratGnDqlWrTO4/12q1VK9eHR8fH4vbLdE6ifkkORRCCCGEKF/PPPMMAJGRkVSrVg1FUUql3ftKEoUQRbNkmZqEhATQ68s4IiGEEI+y6tWr89dff/HNN99w4cIFfv75Z6pUqcL333+Pv78/Tz31lEXtSZIoRBmxZJmahJir2Dq7Yf8A4hJCCPFoWrVqFW+88QY9e/bk8OHDZGVlAZCUlMSnn37KH3/8YVF7srKuEGWouMvUWNuVbKFTIYQQIt+UKVP4+uuv+fbbb7GysjKWt2jRgsOHD1vcnkVJYk5ODm3btiUiIsLiF7qXzz77DEVRGD58uLEsMzOTQYMG4ebmhoODA926dSMmJsbkeVFRUXTq1Ak7Ozs8PDwYPXo0ubm5JnV27NhBkyZNsLa2platWixZsqTU4xdCCCGEKE9nzpyhZcuWZuXOzs4kJiZa3J5FSaKVlRXHjh2z+EXu5cCBA3zzzTc0atTIpHzEiBH89ttv/Pzzz+zcuZNr167x0ksvGc/n5eXRqVMnsrOz2bNnD0uXLmXJkiVMmDDBWCcyMpJOnTrRunVrwsPDGT58OG+99RabNm0q9X4IIYQQQpQXLy8vzp07Z1a+e/duatSoYXF7Fl9ufv3111m4cKHFL1SU1NRUevbsybfffkulSpWM5UlJSSxcuJCZM2fSpk0bmjZtyuLFi9mzZw979+4FYPPmzZw6dYr//e9/BAUF8dxzz/Hxxx8zb948srOzAfj666/x9/dnxowZ1K1bl8GDB/Pyyy/zxRdflFofhPi30+l0xMfHW3TodLryDlsIIR4p/fv3Z9iwYezbtw9FUbh27RrLly9n1KhRDBw40OL2LJ64kpuby6JFi/jzzz9p2rQp9vamt9rPnDnTovYGDRpEp06daNeuHVOmTDGWHzp0iJycHNq1a2csCwwMpFq1aoSFhfHEE08QFhZGw4YN8fT0NNYJDQ1l4MCBnDx5kuDgYMLCwkzayK9T8LL2nbKysow3ewLG9SCFEIVLTExk5vojxd7+T/aSFkKI0jd27Fh0Oh1t27YlPT2dli1bYm1tzejRo3nrrbcsbs/iJPHEiRM0adIEgLNnz5qcs3Rdnh9//JHDhw9z4MABs3PR0dFotVpcXFxMyj09PYmOjjbWKZgg5p/PP3e3OsnJyWRkZGBra2v22lOnTi3RyuRC/JvlT9IRQghRPhRF4YMPPmD06NGcO3eO1NRU6tWrxzfffIO/v78xNyoui5PE7du3W/qUQl2+fJlhw4axZcsWkz0GK4Jx48aZ7EN99epV6tWrV44RPRp0Op1FN87K2oFCCCHEvWVlZTFp0iS2bNliHDns0qULixcvpmvXrqjVakaMGGFxuyVeJ/HcuXOcP3+eli1bYmtri16vt2gk8dChQ8TGxhpHJcEwEWXXrl3MnTuXTZs2kZ2dTWJiosloYkxMDF5eXoDhBs39+/ebtJs/+7lgnTtnRMfExODk5FToKCKAtbU11tbWxsfJycnF7pcomqWXJGXtQCGEEOLeJkyYwDfffEO7du3Ys2cP//nPf+jbty979+5lxowZ/Oc//0GtVlvcrsVJ4s2bN+nevTvbt29HURQiIiKoUaMG/fr1o1KlSsyYMaNY7bRt25bjx4+blPXt25fAwEDee+89fH19sbKyYuvWrXTr1g0wTO2OiooiJCQEgJCQED755BNiY2Px8PAAYMuWLTg5ORlH/kJCQswWj9yyZYuxDfFgWXJJMj0lqWyDEUIIIR4BP//8M8uWLaNz586cOHGCRo0akZuby9GjR+9riz6LZzePGDECKysroqKisLOzM5a/8sorbNy4sdjtODo60qBBA5PD3t4eNzc3GjRogLOzM/369WPkyJFs376dQ4cO0bdvX0JCQnjiiScAaN++PfXq1eONN97g6NGjbNq0iQ8//JBBgwYZRwLfeecdLly4wJgxY/jnn3/46quvWLlyZYmGXYUQQgghKporV67QtGlTABo0aIC1tTUjRoy47z2cLR5J3Lx5M5s2baJq1aom5QEBAVy6dOm+grnTF198gUqlolu3bmRlZREaGspXX31lPK9Wq1m/fj0DBw4kJCQEe3t7evfuzeTJk411/P39+f333xkxYgSzZ8+matWqfPfdd4SGhpZqrEIIIYQQ5SEvLw+tVmt8rNFocHC4/528LE4S09LSTEYQ88XHx5vcx1cSO3bsMHlsY2PDvHnzmDdvXpHPqV69+j33ImzVqhVHjhy5r9iEEEII8e/y2WefMW7cOIYNG8asWbMAw25wo0aN4scffzQZwMpfSSU+Pp7evXuzfft2AgICWLRoEcHBwcY2Bw0aRI0aNRg1alSpxanX6+nTp48xD8vMzOSdd94xW6Zw9erVFrVr8eXmp59+mmXLlhkfK4qCTqdj+vTptG7d2tLmhBBCCCEqnJLuBvfJJ5+QkpLC4cOHadWqFf379zee27t3L/v27bvrWs0l0bt3bzw8PHB2dsbZ2ZnXX38dHx8f4+P8w1IWjyROnz6dtm3bcvDgQbKzsxkzZgwnT54kPj6ev//+2+IAhBBCCCEqkoK7wRXc6CN/N7gVK1bQpk0bABYvXkzdunXZu3cvTzzxBKdPn6ZHjx7Url2bAQMGsGDBAgBycnJ45513+O6770o00/huFi9eXKrt5bM4SWzQoAFnz55l7ty5ODo6kpqayksvvcSgQYPw9vYuixgrjNzcXHJycgBQqVSo1Wry8vJMthfLL8/NzUVfYI0/tVqNSqUqsjy/3Xwajcb4msUpt7KyQqfTkZeXZyxTFAWNRlNkeVGxl1WfDHX0KPo80/JbA9oKhW3Tdmd9Bb2iAr3+jvpK/tlC6yt6HXA7Rv2t+qo76utRgaIUUl9VdP1CYjeU683rK2qLY0evQ0F/z/p6FCi0r4YY8/LyTN6T0vze0+v1qNAViKfo90mvqFDQy8+T9En6JH16IH0Cw85pBZezu3Opuzvdz25wjRs3Ztu2bbz11lts2rTJOBI5ffp0WrVqRbNmzYp83YqmROskOjs788EHH5R2LBVeWFiY8X7MatWqERwczLFjx4iKijLWqVOnDoGBgezfv5+4uDhjeVBQENWrV2fXrl0m2/yFhITg4eHB5s2bTX6wWrduja2trdn9lh07diQjI8NkUXONRkOnTp24ceMGYWFhxnJHR0fatGnD5cuXCQ8PN5a7u7vz5JNPEhERwZkzZ4zlZd2nJk2aoEKPf4bpTj2RtrXR6HPxzbxgLNOhIhYbnNS5JvWzFS1XbGvimJeIe/btlePTVfbEAt7aTKoUqJ+sduaGtQ9u2dE45d1eUidBU5k4oJZNKs4ZicbyOK0XKZpKVMmMRKvPNpZft/Y19Nk+EXWB+pdtapCraArtk61KTyOHRLhVX4eKi3Z1sNWl4Z112aRPsWhw02SbtJOusifaphqVcm5SKfeGSZ9igerW6bgXqJ+gqUyC1h3PrCvY6dJM+pSGQnh4OOnp6cby0vzeS0xMpL4mBjJijH0q6n2KtqmGuyrV5HtVfp6kT9In6VNZ9Cm/7Ts3xJg4cSKTJk2iMPe7G9zYsWMZOHAgNWvWxM/Pj4ULFxIREcHSpUsJCwvjnXfeYfPmzTRr1oxvv/22RJeBHxRFr7d8S4uEhAQWLlzI6dOnAcMXv2/fvo/sPqxXrlzB19eXyMhIqlSpAsh/aiXpU3JyMvN3nMPB0cmkvKjRuNhrV1BZaans7lGgtOgRqthrl1FbaXErpH5hI4lx1y6jsdLiWqD+3UYS465FFV6/kNgN9S+hsbI2rV/ESOLdYi9sJLGw+ncbSUxLSeLtlv4mv9hK83vv5s2bfLMjAjvH/F92dx9JTE9OoP/T/lSqVAmQnyfpk/RJ+lQ2fbp48SL+/v6cOnXK+Pcbih5JvHz5Ms2aNWPLli3GEcBWrVoRFBTErFmzWLFiBX379iUrK8vkeY8//jitW7dm2rRpZm0CtGnThmHDhnHp0iXWr1/P77//Tv/+/XFzcyv2+tLlweKRxF27dvHCCy/g7OxsHDKdM2cOkydP5rfffqNly5alHmRFodFosLKyMilTq9WF3luQ/8NS3PI72y1JuUqlQqUyn4tUVHlRsZdVnwzrNSmGRKkQegorL6K+ohRaX19Efb1S+BwtXWnVLyL2QutbGDuKisL+k7O0r2q1utDvm9L43lMUBR0qi/oqP0/SJ+mT9OlB9cnR0REnJ6dC6xRUGrvB3Wnx4sW4uLjw4osv8tJLL9GlSxesrKz4z3/+w4QJE+4ZU3myOEkcNGgQr7zyCvPnzze+UXl5efz3v/9l0KBBZruoCCGEEEI8DEpjN7iC4uLimDx5Mrt37wZM7w3PyckxGUWtiCxOEs+dO8cvv/xiksmr1WpGjhxpsjSOEEIIIcTDJH83uIIK7gYHGHeDc3V1xcnJiSFDhpjsBlfQ8OHDGTVqlPFSd4sWLfj+++9p3749CxYsoEWLFmXfqftgcZLYpEkTTp8+TZ06dUzKT58+TePGjUstMFHx6XQ6EhMTi10/ISEBLL8FVgghhKgw7rUbXL5NmzZx7tw5vv/+e2PZ4MGDOXjwIM2bN+fxxx9n4sSJDzJ0ixUrSTx27Jjx86FDhzJs2DDOnTtnzJr37t3LvHnz+Oyzz8omSlEhJSYmMnP9EWzsHYtVPyHmKrbObtjfu6oQQghRIZRkNziA0NBQsy2A7ezsWLlyZWmHWGaKlSQGBQWhKIrJzKExY8aY1Xvttdd45ZVXSi86UeHZ2Dti7+RSrLrpKUn3riSEEEKICqFYSWJkZGRZxyGEEEIIISqQYiWJ1atXL+s4hBBCCCFEBVKiHVeuXbvG7t27iY2NNVnUEgz3LAohhBBCiIebxUnikiVLePvtt9Fqtbi5ud1aINlAURRJEoUQQgghHgEWJ4njx49nwoQJjBs3rtBV0oUQFY9OpzMsQWQBFxcX+RkXQoh/MYuTxPT0dHr06CF/PIR4iGSmpTB/601c3NyLXX/k88GP7H7sQggh7s3iJLFfv378/PPPjB07tiziEUKUEUuWKxJCCCEsThKnTp3K888/z8aNG2nYsKHZRt4zZ84steCEEEIIIUT5KFGSuGnTJuO2fHdOXBFCCCGEEA8/i5PEGTNmsGjRIvr06VMG4QghhBBCiIrA4tkn1tbWtGjRoixiEUIIIYQQFYTFI4nDhg3jyy+/ZM6cOWURjxCiArB0yZyEhAQosLe7EEKIh5/FSeL+/fvZtm0b69evp379+mYTV1avXl1qwQkhyoelS+YkxFzF1tkN+zKOSwghxINjcZLo4uLCSy+9VBaxCCEqEEuWzElPSSrbYIQQQjxwFieJixcvLos4hBBCCCFEBSLbpgghhBBCCDMWjyT6+/vfdT3ECxcu3FdAQgghhBCi/FmcJA4fPtzkcU5ODkeOHGHjxo2MHj26tOISQgghhBDlqERL4BRm3rx5HDx48L4DEkIIIYQQ5a/U7kl87rnnWLVqVWk1J4QQQgghylGpJYm//PILrq6updWcEEIIIYQoRxZfbg4ODjaZuKLX64mOjiYuLo6vvvqqVIMTQgghhBDlw+IksUuXLiaPVSoV7u7utGrVisDAwNKKSwghhBBClCOLk8SJEyeWRRxCiH8RS/eGBsNuTyqVLO0qhBAPisVJohBC3C9L94bOTEth5PPBct+zEEI8QMVOElUq1V0X0QZQFIXc3Nz7DkoI8eizZG9oIYQQD16xk8Q1a9YUeS4sLIw5c+ag0+lKJSghhBBCCFG+ip0kvvjii2ZlZ86cYezYsfz222/07NmTyZMnl2pwQgghhBCifJToLvBr167Rv39/GjZsSG5uLuHh4SxdupTq1auXdnxCCCGEEKIcWJQkJiUl8d5771GrVi1OnjzJ1q1b+e2332jQoEFZxSeEEEIIIcpBsS83T58+nWnTpuHl5cUPP/xQ6OVnIYQQQgjxaCh2kjh27FhsbW2pVasWS5cuZenSpYXWW716dakFJ4QQQgghykexk8RevXrdcwkcIYQQQgjxaCh2krhkyZIyDEMIIYQQQlQksuPKI06n05GYmGjRc2T7MyGEEEJIkviIS0xMZOb6I9jYOxarvmx/JoQQQgiQJPFfQbY/E0IIIYSlJEkUQjxyLL3NQm6xEEIIc5IkCiEeOZbcZiG3WAghROEkSRRCPJLkNgshhLg/cn1FCCGEEEKYkSRRCCGEEEKYkcvNQoh/NZ1OR0JCgkXPkYkuQoh/A0kShRD/aplpKczfehMXN/di15eJLkKIfwNJEoUQ/3oyyUUIIcxJkiiEqPAsvSSckJAAen0ZRiSEEI8+SRKFEBWepZeEE2KuYuvshn0ZxyWEEI8ySRKFEA8FSy4Jp6cklW0wQgjxLyBJojBhyWU9uaQnhBBCPLokSRQmLLmsJ5f0hBBCiEeXJInlTKfTkZiYaNFzynqNtuJe1pNLekIIIcSjS5LEcpaYmMjM9UewsXcsVn1Zo00IIYQQD4IkiRWArNEmhBBCiIpG9pUSQgghhBBmJEkUQgghhBBmJEkUQgghhBBmJEkUQgghhBBmyjVJnDp1Ko899hiOjo54eHjQpUsXzpw5Y1InMzOTQYMG4ebmhoODA926dSMmJsakTlRUFJ06dcLOzg4PDw9Gjx5Nbm6uSZ0dO3bQpEkTrK2tqVWrFkuWLCnr7gkhhBBCPLTKNUncuXMngwYNYu/evWzZsoWcnBzat29PWlqasc6IESP47bff+Pnnn9m5cyfXrl3jpZdeMp7Py8ujU6dOZGdns2fPHpYuXcqSJUuYMGGCsU5kZCSdOnWidevWhIeHM3z4cN566y02bdr0QPsrhHj45e9KFB8fX+xDp9OVd9hCCGGxcl0CZ+PGjSaPlyxZgoeHB4cOHaJly5YkJSWxcOFCVqxYQZs2bQBYvHgxdevWZe/evTzxxBNs3ryZU6dO8eeff+Lp6UlQUBAff/wx7733HpMmTUKr1fL111/j7+/PjBkzAKhbty67d+/miy++IDQ09IH3Wwjx8LJkV6L8+rK2qRDiYVSh7klMSjLs4JH/y/TQoUPk5OTQrl07Y53AwECqVatGWFgYAGFhYTRs2BBPT09jndDQUJKTkzl58qSxTsE28uvkt3GnrKwskpOTjUdKSkrpdVII8dDLX9u0OEdxF8oXQoiKpsIkiTqdjuHDh9OiRQsaNGgAQHR0NFqtFhcXF5O6np6eREdHG+sUTBDzz+efu1ud5ORkMjIyzGKZOnUqzs7OxqNevXql0kchhBBCiIdFhUkSBw0axIkTJ/jxxx/LOxTGjRtHUlKS8Th16lR5hySEEEKIMlYaE2rj4+N54YUXcHBwIDg4mCNHjpg8f9CgQcbb3yq6CpEkDh48mPXr17N9+3aqVq1qLPfy8iI7O5vExEST+jExMXh5eRnr3DnbOf/xveo4OTlha2trFo+1tTVOTk7Gw9FRLhcJIUpGJroI8fAojQm1n3zyCSkpKRw+fJhWrVrRv39/47m9e/eyb98+hg8f/iC7VWLlOnFFr9czZMgQ1qxZw44dO/D39zc537RpU6ysrNi6dSvdunUD4MyZM0RFRRESEgJASEgIn3zyCbGxsXh4eACwZcsWnJycjJeJQ0JC+OOPP0za3rJli7ENIYQoKzLRRYiHR2lMqD19+jQ9evSgdu3aDBgwgAULFgCQk5PDO++8w3fffYdarX7gfSuJck0SBw0axIoVK/j1119xdHQ03kPo7OyMra0tzs7O9OvXj5EjR+Lq6oqTkxNDhgwhJCSEJ554AoD27dtTr1493njjDaZPn050dDQffvghgwYNwtraGoB33nmHuXPnMmbMGN588022bdvGypUr+f333y2KNzc3l5ycHABUKhVqtZq8vDyT//rzy3Nzc9Hr9cZytVqNSqUyK8//XNHnmbyW/tYgr8KdIwp69Hq9MY58VlZW6HQ68vJut6Moyq029He0r6BXVKDXoaA3KS+svh4FFBWKXgcF6uc/985yPSpQFLM+GercGcvd+koh9fNj199R/+59NY/RUF9l1tf82AvpU1H1C4ndUK43r6+oLY79ft+nu8ZeyPtUZOxF9VVRF1K/6PdJr6hQiqhf6PukqFDdUf+u75OioEZv8vUprffpbrEX9j7l17ezd8Dh1hWJu75PioIKnfF3Tf7vF5VKZfYzr9EYfn3fuSZsUeVF/Y7QaDRFlhf1++1+f+/ll0ufpE/32ye1Wo2NjQ06na7IPgGkpKSQnJxsPG9tbW3MEe7G0gm1TzzxBI0bN2bbtm3GpfYaNWoEwPTp02nVqhXNmjW75+tWFOWaJM6fPx+AVq1amZQvXryYPn36APDFF1+gUqno1q0bWVlZhIaG8tVXXxnrqtVq1q9fz8CBAwkJCcHe3p7evXszefJkYx1/f39+//13RowYwezZs6latSrfffedxcvfhIWFYWdnB0C1atUIDg7m2LFjREVFGevUqVOHwMBA9u/fT1xcnLE8KCiI6tWrs2vXLpPZ0vmTdKpnnENV4A/SZZsa5Coa/DPOmsRwEk/S09PZvXu3sUyj0dCpUydu3LhhMmPb0dGRoKAgXJQMqmZEG8vTVfZE21SjUs5NKuXeMJYnq52JBapbp+Ne4HUTNJVJ0LrjmXUFO93tIXe9VstNPVTJjESrzzaWX7f2JUPtYNanFJUNOWDWp0jb2mj0ufhmXjCW6VARiw1O6lyT+tmKliu2NXHMS8Q927RPsYC3NpMqBeonq525Ye2DW3Y0TnlJJn2KA2rZpOKckWgsj9N6kaKpVGifAILsE1EXqF/U+xRpWxtblZ5GDolwq74OFRft6mCrS8M767JJn2LR4KbJNmmntN6nOK0XcUA922RsC8Re1Pt02aYGaqBpgdjz+1TY+3TRrg7OGh2BBerf7X2KtqmGj00OVW0zjPXv9j4laN0JcMjCxep2/bu9TxlqB4JdMlArt+uX1vt0xbYmlbV51LC/Xf9u79MNax/87LLxsL4dy93epxRNJWqqbxIWFoOjoyOOjo7Y2tqiVqvJyMgw+SNoY2ODoihmk+9sbW3R6/VkZmYayxRFwdbWlry8PLKysozlKpUKGxsbcnNzyc6+/XVUq9VYW1uTk5NjkiBoNBq0Wi3Z2dkmiYCVlRVWVlZkZWWZ/MHXarVoNBoyMzNN/rBbW1tLn6RPpdYna2trbt68Wejf3PDwcACzCagTJ05k0qRJ3E1JJ9SOHTuWgQMHUrNmTfz8/Fi4cCEREREsXbqUsLAw3nnnHTZv3kyzZs349ttvcXZ2vmsc5UnRF3znRaGuXLmCr68vkZGRVKlSBSi9/9SSk5OZv+O8cZQhX1EjH6kpyQxsVQsnJyeT8qL+U0tOTmb+9gjsHQvWL3rkI/baZdRWWtzcPQrEUvjIR9y1yyhWNlR296CwEZE7R6hir11GZWV9q/69+xp77QoqK+0d9YseoSos9ruNUMVdu4zGSourSV+LHqGKuxZVeP1CYjfUv4TGytq0fhEjVHeL/X7fp7vGXsRIYqGx32UkMe7qxTvq330k8cbVi6gLqV/USOLNqxdRFah/r5HE+KsXUe6sX1jsFr5Pd4u9qJHEO+vfayQxIzme7o0rk52djbu7O/b29qhUKrP7FPOvFNz5K7yocpVKhV6vNylXFAVFUcqtXPokfbrfcr1ez/Xr11Gr1VSpUsUYQ/7f3IsXL+Lv78+pU6eMf7+heCOJAwcOZMOGDezevds4X2LFihX07dvXJOEFePzxx2ndujXTpk0rtK02bdowbNgwLl26xPr16/n999/p378/bm5uFXoSS7mOJD5sNBoNVlZWJmVqtbrQewvyh93vVW78oVIKvz9Bz53lhh+UO+MAww+iSmU+F0mPUnj7igq9eWmR9fWKyqyeUkj57frmsVNULBTWV4quryiF1i9u7Pl0pVW/iNgLrW9h7Pf7Pt0z9kLfjyJip/T6Wlj9u8Ve2PdCUfXzUFAVVr8MY7/b+2RJX1WKiszMTLy9vXFzcyu0jhDiNnd3d65du1bo38b8v7mOjo5mgyt3kz+hdteuXUVOqC04mlhwQu2dFi9ejIuLCy+++CIvvfQSXbp0wcrKiv/85z8mu8NVRBVidrMQQggD7a1cMv/WFiHE3Wm1WgCTK2klpdfrGTx4MGvWrGHbtm13nVCb784JtQXFxcUxefJkvvzyS2OM+ZfR8+87rshkJFEIISoQvV5nvHXkzgkARVGr1carEkL825Tm935pTKgtaPjw4YwaNcp4qbtFixZ8//33tG/fngULFtCiRYtSi70sSJIohBAVSHZGGqmZWuLTstHk3PuPnz4vD08XuyJvcRFCFF9pTKjNt2nTJs6dO8f3339vLBs8eDAHDx6kefPmPP7440ycOLHM+lIa5LeKEEJUMIpKQXXH/c46nY6kOzYWAMjT5WGlyyyTJNHFxaXQ+5wrqvxJCkeOHCEoKKi8wwHgn3/+oU+fPoSHhxMYGGicbVue/Pz8GD58uHFBZ0VRWLNmDV26dClxm6XRRkVQnLm8NjY2zJs3j3nz5t21XmhoqNkqKnZ2dqxcufK+YnyQJEkUQoiHQFJiIgv+PI6NvelKCDq9HnutGqWIiTAlVZJFvfv06cPSpUuZOnUqY8eONZavXbuWrl27FusP8KNm4sSJ2Nvbc+bMGRwcHMo7nEJdv36dSpUqFavupEmTWLt2rVmya0kb4uEhSaIQQjwkbOwdsXN0MSnT6XXYaTUVZsTPxsaGadOm8fbbbz8ySUN2drZxcoSlzp8/T6dOnahevXqFielORc3KfdBtiIqnYvxWEUII8Uho164dXl5eTJ06tcg6kyZNMrscPGvWLPz8/IyP+/TpQ5cuXfj000/x9PTExcWFyZMnk5uby+jRo3F1daVq1aosXrzYrP1//vmHJ598EhsbGxo0aMDOnTtNzp84cYLnnnsOBwcHPD09eeONN7hx4/ZC6K1atWLw4MEMHz6cypUrF7nxgk6nY/LkyVStWhVra2uCgoJMtnVTFIVDhw4xefJkFEUpcvHm/NcbPHgwzs7OVK5cmfHjx5uMvPr5+fHxxx/Tq1cvnJycGDBgAAC7d+/m6aefxtbWFl9fX4YOHWqyz3BsbCwvvPACtra2+Pv7s3z5crPXVxSFtWvXGh9fuXKFV199FVdXV+zt7WnWrBn79u1jyZIlfPTRRxw9etS4buGSJUsKbeP48eO0adMGW1tb3NzcGDBgAKmpqcbz+e/v559/blzuadCgQSYLaH/11VcEBARgY2ODp6cnL7/8cqFfP1F2JEkUQghRatRqNZ9++ilffvklV65cua+2tm3bxrVr19i1axczZ85k4sSJPP/881SqVIl9+/bxzjvv8Pbbb5u9zujRoxk1ahRHjhwhJCSEF154gZs3bwKQmJhImzZtCA4O5uDBg2zcuJGYmBi6d+9u0sbSpUvRarX8/ffffP3114XGN3v2bGbMmMHnn3/OsWPHCA0NpXPnzkRERACGS7D169dn1KhRXL9+nXfffbfIvi5duhSNRsP+/fuZPXs2M2fO5LvvvjOp8/nnn9O4cWOOHDnC+PHjOX/+PB06dKBbt24cO3aMn376id27dzN48GDjc/r06cPly5fZvn07v/zyC1999RWxsbFFxpGamsozzzzD1atXWbduHUePHmXMmDHodDpeeeUVRo0aRf369bl+/TrXr1/nlVdeMWsjLS2N0NBQKlWqxIEDB/j555/5888/TeIC2L59O+fPn2f79u0sXbqUJUuWGJPOgwcPMnToUCZPnsyZM2fYuHEjLVu2LDJuUTbkcrMQQohS1bVrV4KCgpg4cSILFy4scTuurq7MmTMHlUpFnTp1mD59Ounp6bz//vsAjBs3js8++4zdu3fTo0cP4/MGDx5Mt27dAMNs1Y0bN7Jw4ULGjBnD3LlzCQ4O5tNPPzXWX7RoEb6+vpw9e5batWsDEBAQwPTp0+8a3+eff857771nfO1p06axfft2Zs2axbx58/Dy8kKj0eDg4HDPy7G+vr588cUXKIpCnTp1OH78OF988QX9+/c31mnTpg2jRo0yPn7rrbfo2bOncQJKQEAAc+bM4ZlnnmH+/PlERUWxYcMG9u/fz2OPPQbAwoULqVu3bpFxrFixgri4OA4cOGC8H7VWrVrG8w4ODmg0mrv2Z8WKFWRmZrJs2TLs7e0BmDt3Li+88ALTpk3D09MTgEqVKjF37lzUajWBgYF06tSJrVu30r9/f6KiorC3t+f555/H0dGR6tWrExwcfNevoSh9MpIohBCi1E2bNo2lS5dy+vTpErdRv359k3stPT09adiwofGxWq3Gzc3NbGSs4KLGGo2GZs2aGeM4evQo27dvx8HBwXgEBgYChvsH8zVt2vSusSUnJ3Pt2jWzde5atGhRoj4/8cQTJuv9hYSEEBERYbLYcrNmzUyec/ToUZYsWWLSl9DQUHQ6HZGRkZw+fRqNRmPSl8DAQLN9hwsKDw8nONiyCUt3On36NI0bNzYmiGD4uuh0Os6cOWMsq1+/vskMfm9vb+N7+eyzz1K9enVq1KjBG2+8wfLly0lPTy9xTKJkJEkUQghR6lq2bEloaCjjxo0zO5e/l29BBe9Fy3fnFmuFbbtW2N7Cd5OamsoLL7xAeHi4yREREWFyObNgglNR3BlTamoqb7/9tkk/jh49SkREBDVr1izRa9ja2pZGqMVyt/fS0dGRw4cP88MPP+Dt7c2ECRNo3LgxiYUsAyXKjiSJQgghysRnn33Gb7/9RlhYmEm5u7s70dHRJoliaa4fuHfvXuPnubm5HDp0yHiJtUmTJpw8eRI/Pz9q1aplcliSGDo5OeHj48Pff/9tUv73339Tr149i2Pet2+fWR8CAgJMRtru1KRJE06dOmXWj1q1aqHVagkMDDT2P9+ZM2fummg1atSI8PBw4uPjCz2v1WrvuZVc3bp1OXr0qMkEmr///tt420BxaTQa2rVrx/Tp0zl27BgXL15k27ZtxX6+uH+SJAohxEMiMy2F9JTEO44k0lMSSUsu3SMzLeW+423YsCE9e/Zkzpw5JuWtWrUiLi6O6dOnc/78eebNm8eGDRvu+/XyzZs3jzVr1vDPP/8waNAgEhISePPNNwHDtmvx8fG8+uqrHDhwgPPnz7Np0yb69u1r8T66o0ePZtq0afz000+cOXOGsWPHEh4ezrBhwyyOOSoqipEjR3LmzBl++OEHvvzyy3u2895777Fnzx4GDx5sHA399ddfjRNE6tSpQ4cOHXj77bfZt28fhw4d4q233rrraOGrr76Kl5cXXbp04e+//+bChQusWrXKmOj7+fkRGRlJeHg4N27cICsry6yNnj17YmNjQ+/evTlx4gTbt29nyJAhvPHGG8b7Ee9l/fr1zJkzh/DwcC5dusSyZcvQ6XQWJZni/snEFSGEeAg4u7gwoF1Ds/K8vFwq2Wos2nGluHs93+3eteKaPHkyP/30k0lZ3bp1+eqrr/j000/5+OOP6datG++++y4LFiy479cDwwjmZ599Rnh4OLVq1WLdunVUrlwZwDj6995779G+fXuysrKoXr06HTp0sHityaFDh5KUlMSoUaOIjY2lXr16rFu3joCAAItj7tWrFxkZGTz++OOo1WqGDRtmXOamKI0aNWLnzp188MEHPP300+j1emrWrGky43jx4sW89dZbPPPMM3h6ejJlyhTGjx9fZJtarZbNmzczatQoOnbsSG5uLvXq1TPuLtKtWzdWr15N69atSUxMNNmuLp+dnR2bNm1i2LBhPPbYY9jZ2dGtWzdmzpxZ7K+Hi4sLq1evZtKkSWRmZhIQEMAPP/xA/fr1i92GuH+K/t+4BL6Frly5gq+vL5cvX6Zq1aql2nZ8fDxfbT+HvZNLseqnJSfy39a1in1TsaXtx129hMrKGjePey+Makldqf/wxPJvq1+RYgHIvHGZVgGu+PrVwEprfc/6OdnZ6PU61Bqre9YF2eu5ImrVqhVBQUHMmjWrvEN5KGVmZhIZGYm/vz82NjYm58ry7/e/gfyWEEKIh5xKpb7rvWsFWXZBVQjxbyb3JAohhBBCCDMykiiEEEKUox07dpR3CEIUSkYShRBCCCGEGUkShRBCCCGEGUkShRBCCCGEGUkShRBCCCGEGUkShRBCCCGEGUkShRBCCCGEGUkShRBC/CsoisLatWvLO4z7NmnSJIKCgso7DPEvIEmiEEL8m+j15ObmWnRYuntrWFgYarWaTp06WRyen5/fQ7c9XatWrRg+fHiZtF1YYvvuu++ydevWMnk9IQqSxbSFEOJfRKfTEZeSWaZ7PS9cuJAhQ4awcOFCrl27ho+PT0nDLVfZ2dlotdryDsOMg4MDDg4O5R2G+BeQkUQhhKjg9Ho9Gdl5pXZk5UJ2XvEOVJb9mUhNTeWnn35i4MCBdOrUiSVLlpjV+e2333jsscewsbGhcuXKdO3aFTCMyF26dIkRI0agKAqKogCFX16dNWsWfn5+xscHDhzg2WefpXLlyjg7O/PMM89w+PBhi2Jv1aoVgwcPZvjw4VSuXJnQ0FAATpw4wXPPPYeDgwOenp688cYb3LhxA4A+ffqwc+dOZs+ebYz54sWL93xe/usNHTqUMWPG4OrqipeXF5MmTTKez+9f165dURTF+PjOr4dOp2Py5MlUrVoVa2trgoKC2Lhxo/H8xYsXURSF1atX07p1a+zs7GjcuDFhYWEWfX3Ev4+MJAohRAWXmaOj1ec7yuW1t4542qL6K1euJDAwkDp16vD6668zfPhwxo0bZ0z4fv/9d7p27coHH3zAsmXLyM7O5o8//gBg9erVNG7cmAEDBtC/f3+LXjclJYXevXvz5ZdfotfrmTFjBh07diQiIgJHR8dit7N06VIGDhzI33//DUBiYiJt2rThrbfe4osvviAjI4P33nuP7t27s23bNmbPns3Zs2dp0KABkydPBsDd3f2ezyv4eiNHjmTfvn2EhYXRp08fWrRowbPPPsuBAwfw8PBg8eLFdOjQAbVaXWjMs2fPZsaMGXzzzTcEBwezaNEiOnfuzMmTJwkICDDW++CDD/j8888JCAjggw8+4NVXX+XcuXMWjRKLfxf5zhBCCFFqFi5cyOuvvw5Ahw4dSEpKYufOnbRq1QqATz75hB49evDRRx8Zn9O4cWMAXF1dUavVODo64uXlZdHrtmnTxuTxggULcHFxYefOnTz//PPFbicgIIDp06cbH0+ZMoXg4GA+/fRTY9miRYvw9fXl7Nmz1K5dG61Wi52dnUnMc+fOvefzABo1asTEiRONrz137ly2bt3Ks88+i7u7OwAuLi53/Xp8/vnnvPfee/To0QOAadOmsX37dmbNmsW8efOM9d59913jfaIfffQR9evX59y5cwQGBhb76yP+XSRJFEKICs7GSsWOd1sVei4nOxtFUdBYFe8eQ0vrW6mKP2nlzJkz7N+/nzVr1gCg0Wh45ZVXWLhwoTFJDA8Pt3iUsDhiYmL48MMP2bFjB7GxseTl5ZGenk5UVJRF7TRt2tTk8dGjR9m+fXuh9wCeP3/emOzdqbjPa9Sokck5b29vYmNjix1vcnIy165do0WLFiblLVq04OjRoyZlBV/L29sbgNjYWEkSRZEkSRRCiApOURRstYVfatSgvpX0FX7+fuvn5eUVO86FCxeSm5trMlFFr9djbW3N3LlzcXZ2xtbWttjt5VOpVGYzrHNyckwe9+7dm5s3bzJ79myqV6+OtbU1ISEhZGdnW/Ra9vb2Jo9TU1N54YUXmDZtmlnd/ESrMMV9ntUdybqiKOh0OotiLq6Cr5V/+b+sXks8GiRJFEIIcd9yc3NZtmwZM2bMoH379ibnunTpwg8//MA777xDo0aN2Lp1K3379i20Ha1Wa5aYuru7Ex0djV6vNyY34eHhJnX+/vtvvvrqKzp27AjA5cuXTSaJlFSTJk1YtWoVfn5+Rd67V1jMxXlecVhZWd01UXdycsLHx4e///6bZ555xlj+999/8/jjj5f4dYUAmd0shBCiFKxfv56EhAT69etHgwYNTI5u3bqxcOFCACZOnMgPP/zAxIkTOX36NMePHzcZbfPz82PXrl1cvXrVmOS1atWKuLg4pk+fzvnz55k3bx4bNmwwef2AgAC+//57Tp8+zb59++jZs2eJRi3vNGjQIOLj43n11Vc5cOAA58+fZ9OmTfTt29eYvPn5+bFv3z4uXrzIjRs30Ol0xXpecfj5+bF161aio6NJSEgotM7o0aOZNm0aP/30E2fOnGHs2LGEh4czbNiw++6/+HeTJFEIIcR9W7hwIe3atcPZ2dnsXLdu3Th48CDHjh2jVatW/Pzzz6xbt46goCDatGnD/v37jXUnT57MxYsXqVmzpnHiRt26dfnqq6+YN28ejRs3Zv/+/bz77rtmr5+QkECTJk144403GDp0KB4eHvfdr/xRury8PNq3b0/Dhg0ZPnw4Li4uqG4tD/Tuu++iVqupV68e7u7uREVFFet5xTFjxgy2bNmCr68vwcHBhdYZOnQoI0eOZNSoUTRs2JCNGzeybt06k5nNQpSEord0Kf1/oStXruDr68vly5epWrVqqbYdHx/PV9vPYe/kUqz6acmJ/Ld1LVxdXcuk/birl1BZWePmce+ZhZbUlfoPTyz/tvoVKRaAzBuXaRXgiq9fDay01vesX9YTV/Jyc3G10xT7kqlarTZeEhbiQcjMzCQyMhJ/f39sbGxMzpXl3+9/A7knUQghRJEs2aGlJLuzCCEqLvlJFkIIcVcqlbrIhZwLKv6ddkKIh4HckyiEEEIIIcxIkiiEEEIIIcxIkiiEEEIIIcxIkiiEEEIIIcxIkiiEEEIIIcxIkiiEEEIIIcxIkiiEEEIIIcxIkviQ0el0JCQkEB8fX6wjISEBZFMdIcQjpk+fPnTp0sX4uFWrVgwfPvyBx7Fjxw4URSExMfGBv3ZpK6+voai4ZDHth0xmWgrzt97Exc29WPUTYq5i6+yGfRnHJYQQwwe9zc8/LAfAysqKatWq0atXL95///0y34Vl9erVWBVzq8EdO3bQunVrEhIScHFxKdO47oeiKKxZs8YkGS4NRfXfkq+h+HeQJPEhZGPvWOy9mNNTkso2GCGEyKfX0759exYuXEhWVhYbNmxg6NChqNVq3nvvPbPq2dnZ2Nralspez8Xdz74iyMnJqZDJ2MP0NRQPhlxuFkKIik6vh5y0Qg8lJx0lJ73I8/db35LbVfR6QKVBZV8JW1cvXurZl6efac3qteuITcnitV59eP7Frnww6WOq+voSWLcueXl5XL58me7du+Pi4oKrqysvvvgiFy9eNLabl5fHyJEjcXFxwc3NjTFjxqC/I647L5VmZWXx3nvv4evri7W1NbVq1WLhwoVcvHiR1q1bA1CpUiUURaFPnz6A4XaeqVOn4u/vj62tLY0bN+aXX34xeZ0//viD2rVrY2trS+vWrU3iLIqiKMyfP5/OnTtjb2/PJ598AsCvv/5KkyZNsLGxoUaNGnz00Ufk5uYC4OfnB0DXrl1RFMX4+F7Py3+97777jq5du2JnZ0dAQADr1q0DuGv/7/waJiQk0KtXLypVqoSdnR3PPfccERERxvNLlizBxcWFTZs2UbduXRwcHOjQoQPXr1+/59dEPBxkJFEIISq63HQ8v6xRLi999e0zoC3+DSuKopjs82xrZ0diQgJqtRpFUdi9aweOTk78tPZ3dHl55OTkEBoaSkhICH/99RcajYYpU6bQoUMHjh07hlarZcaMGSxZsoRFixZRt25dZsyYwZo1a2jTpk2RcfTq1YuwsDDmzJlD48aNiYyM5MaNG/j6+rJq1Sq6devGmTNncHJywtbWFoCpU6fyv//9j6+//pqAgAB27drF66+/jru7O8888wyXL1/mpZdeYtCgQQwYMICDBw8yatSoYn1dJk2axGeffcasWbPQaDT89ddf9OrVizlz5vD0009z/vx5BgwYAMDEiRM5cOAAHh4eLF68mA4dOhi/pvd6Xr6PPvqI6dOn83//9398+eWX9OzZk0uXLt21/3fq06cPERERrFu3Dicnp/9v797DoqrzP4C/zwAzMFxEJRglLqZGmOaNRGhTe0JBW9IuZJpPKkarrm3masqjCGbrGmbe0tDtEdzSzEuQaabIRl5DQ7wjXsKflwVkuSigXOf7+4P1LOPMwIDADPp+Pc88Peeczznn85350nz8nu85g9mzZ2PEiBE4d+6cPBJ6584dfPrpp/jqq6+gUCgwbtw4zJw5Exs3bjTpfSHLxiKRiIianRACB1J/RmpKMsLfnSKvV6vt8dmqL6BUKlFTU4MtW7ZAq9Xiyy+/lC87x8fHw9nZGampqRg2bBiWL1+OyMhIvPrqqwCAuLg47Nmzx+i5L1y4gC1btiA5ORlBQUEAgCee+F+Rfe+yqqurqzwnr6KiAosWLcK+ffsQEBAg73Pw4EGsXbsWgwcPxhdffIGuXbti6dKlAAAfHx+cPn0an3zySYPvx9ixYzFx4kR5OTw8HHPmzMH48ePlcy1cuBAffvghoqOj8dhjtfPOnZ2dodFo5P0WLFhQ7373TJgwAWPGjAEALFq0CCtXrsTRo0cREhJisP33u1ccHjp0CIGBgQCAjRs3wsPDA0lJSQgLCwNQe+k8Li4OXbt2BQBMmzYNH330UYPvB7UNLBKJiCydtRp57/1ucFN1ZVXt6J2Naf87b2y80FqjMTMG9+39CU90dkF1VRW0Wi1eCRuNmZHz5O2+PZ6GUqmUl0+dOoVLly7B0dFR5zjl5eW4fPkybt26hZycHPj7+8vbrK2t4efnp3fJ+Z4TJ07AysoKgwcPNjnvS5cu4c6dOxg6dKjO+srKSvTt2xcAkJmZqZMHALmgbIifn5/O8smTJ3Ho0CH50jNQe1m9vLwcd+7cgVqtNngcU/d75pln5O329vZwcnLCzZs3TcoVqG2rtbW1Tns7duwIHx8fZGZmyuvUarVcIAJAp06dGnUesmwsEomILJ0kATaGL/kKUfnf7abdCNHYeFRWmpolACDwD4MQu2wVbJQ20HTqrHdXs9pet/gpLS1F//79DV6evDea1ljGLp/Wp7S0FACwa9cuuLu762xTqVRNyqMue3vdz6+0tBQLFiyQR0frsrW1rTdPU/a7/8YYSZKg1Wobm3aDDJ3HWPFObQ+LRCIiajZqtRpd6owsNaRv377YunUrXF1d4eTkZDCmU6dOSEtLw6BBgwAA1dXVSE9PR79+/QzG9+rVC1qtFr/88ot8ubmueyOZNTU18roePXpApVLh6tWrRkcgfX195RtA7vn1118bbqQB/fr1Q1ZWFrp162Y0xsbGRidHU/driKH238/X1xfV1dVIS0uTLzcXFBQgKysLPXr0aPK5qW3h3c1ERGQeQuCNN96Ai4sLXn75ZaSmpuLixYtISUnBtGnTcOXKFVRXV+O9997D4sWLsX37dmRmZmLq1Kn1Prza29sb48ePR3h4OJKSkpCdnY3U1FRs2bIFAODl5QVJkrBz507k5+ejtLQUjo6OmDlzJj744ANs2LABly9fxvHjx7Fq1Sps2LABADB58mRcvHgRs2bNQlZWFjZt2oSEhIQmNX3+/Pn45z//iQULFuDs2bPIzMzE5s2bMW/e/y7Ne3t7IyUlBbm5ubU/jGDifg0x1P77de/eHSNHjkRERAQOHjyIkydPYty4cXB3d8fIkSOb1GZqe1gkEhGRWWi1WpTVKLBlx094rJM7Xnv9dfTq1QuT3olAcUkZKiQVbpZU4K13puKVsDcxcWI4AgMDYW9vj5EjR0IIgerqalRXV0MIAa1WKy+vWrUKr776KqZOnYqnnnoKERERKCsrAwC4u7vLN4C4ublh2rRpAICFCxciKioKf//73+Hr64uQkBDs2rULXbp0AQB4enpi+/btSEpKQu/evREXF4dFixY1qe3BwcHYuXMn9u7di2effRYDBw7EsmXL4OXlJccsXboUycnJ8PDwkOdFmrJfQ4y1/37x8fHo378//vjHPyIgIABCCPz4448W+YxHahmS4OSBBl2/fh0eHh64du0aHn/88WY9dmFhIdb8fMnkh2Pn3/g/KGxU6OiqaTi4heMtKZe2Hm9JuTxq8ZaUCwCU/+cahnTvAA/vJ2CjbHguXFVlJSRJgrWJX9wtGd8auQihhZW1ifMva2rg5qxu8V97IfMqLy9HdnY2unTpojefsyW/vx8F/MshIqI2Q6Gw0nkOY32Mz7gjIlPwcjMRERER6WGRSERERER6WCQSERERkR7OSSQisiBC1L7Aewof3H/vfm6Me78xTW0H779tOSwSiYgsSHm1FjVagcqKctiojP/yBjVMq9Uiv6Scd0M/5Cr/+6tApt7QRKbjXwIRkQWp1gJXiipgq8oHAChVtrU/o2csvqoKEiQIYdpPrrVkvCXlUjfe1JFBbXU1ysrKGlVscOTRvLRaLfLz86FWs7hvCXxHiYgsTFZBFezsgaqaPFg1UIDU1FRDkiQoFCY+FqYF4y0pl6bGXxeAwsq06fpCK+CkVkKh4PR+c1IoFPD09GSx3gJYJBIRWaBr5Sr8u1xAqRAAjM+5KrqZD8laCecOLiYdtyXjLSmX1ogvK7mF0B72Rn9z2hAnJycWlc1MqWSh3lJYJBIRWagaSLirrX90pKRCC4VWQKU1bbSsJeMtKZfWiC8oLsG61AI4d3zMpPjyshLM+GNfdOjQwaR4InN7pErv1atXw9vbG7a2tvD398fRo0fNnRIREbVhtvaOsHdyNumltLNHUVERCgsLTX5ptabNv6TmVV+9MGPGDHTo0AEeHh7YuHGjzn5bt25FaGhoa6fbYh6ZkcRvv/0WM2bMQFxcHPz9/bF8+XIEBwcjKysLrq6u5k6PiIgecuVlJfgihSOPlq6+eiEtLQ2bNm3C3r17cfHiRYSHhyM4OBguLi64desW5s6di3379pm7Cc3mkSkSP/vsM0RERGDixIkAgLi4OOzatQvr16/HnDlzzJwdERE9Cu6NPJpCq9WiqKjI5FgAjZqb5+zszLl8BtRXLygUCgwZMgR+fn7w8/PD9OnTkZ2dDRcXF3z44YeYMmUKPD09zdyC5vNIFImVlZVIT09HZGSkvE6hUCAoKAhHjhzRi6+oqEBFRYW8fOvWLQDA9evX5QezSpIEKysr1NTU6DzIU6FQQKFQGF1//4Ndb9++jZvXfoedWq2z/t4Fhvv/fIvy8yBZK1FVVnxfvARAGIxXGImXIFB3tpMAUJyfB4WNbnzttHkJivsmzxfn5wEGjw3AQLzx3I231dTcjcXfy/1B26oFcCs/D1b3xRvL3Xh8639O9+IN594cbZVwOz9XLx9jn5Oh+Po+JwEJJf/J1ek79X1OMBDfXJ9TY9ra2NyNxdf3OQFAyX/yTGxrbZv0441/ThIEbteJN+fnZLitxj+n+3M31ta6bTLUVmOfkwICt+rEN/Q5KQAD8cY/p3vxf8+sgtrRGVoBSAAUdZIXgLy+rPg/UFhbw97RGULUnlcB3acnaUXtPgoJqCy/g7HPPQknJ6f/3gFe+71Vl0KhgCRJBtcD0LsUbmy9lZUVhBAG12u1Wvm70tnZWf5u1Wq1OvFN/c69fv06gNrv8bo3GKlUKqhUKtyvoXph6tSpWLduHYqKivD777/j7t276NatGw4ePIjjx49jzZo1esds08Qj4MaNGwKAOHz4sM76WbNmiQEDBujFR0dH37udkC+++OKLL774eshe0dHRTa4XoqOjRdeuXUXPnj3Fd999JyoqKkTPnj3Fb7/9JlatWiWefPJJERgYKM6cOdM8RYwZPRIjiY0VGRmJGTNmyMvV1dXIzMyEh4eH/C+lkpIS9OjRA+fOnYOjo6O5Um2SIUOGIDU1tU2dp6nHaux+psabEldfjLFtbbVftVafau5zsV9ZtrbYrx7kOObqV03d3hb6lVarxdWrV9GjRw+dh20bGkU0VUxMDGJiYuTlBQsWICgoCDY2Nvj4449x+vRp7Ny5E2+//TbS09MfJH2zeySKRBcXF1hZWSEvL09nfV5eHjQajV68oWHo5557Tmf59u3bAAB3d/dGPSPLEiiVSjz++ONt6jxNPVZj9zM13pS4+mKMbWur/aq1+lRzn4v9yrK1xX71IMcxV79q6va20q8aM0ewsfXC+fPn8fXXXyMjIwPr16/HoEGD8Nhjj+GNN95AeHg4SkpKLLaANsUjMWNVqVSif//+SElJkddptVqkpKQgICDAjJmZx5///Oc2d56mHqux+5kab0pcfTGt9Rm0ltZsD/sV+5Uln+tBjmOufvWg2x8mjakXhBD405/+hM8++wwODg6oqalBVVUVAMj/vX8+Z1sjCVFnpudD7Ntvv8X48eOxdu1aDBgwAMuXL8eWLVtw/vx5uLm5Nfp4t2/fRrt27fQmwxI9CPYragnsV9QSHtZ+ZWq98I9//AN79uzBtm3bAABHjx7F0KFDsWfPHuzevRvbtm3D2bNnzdWMZvFIXG4GgNGjRyM/Px/z589Hbm4u+vTpg59++qlJBSJQe0k6Ojr6geY1EN2P/YpaAvsVtYSHtV+ZUi/k5eXhb3/7Gw4fPiyvGzBgAP7617/ipZdegqurKzZs2GCO9JvVIzOSSERERESmeyTmJBIRERFR47BIJCIiIiI9LBKJiIiISA+LRCIiIiLSwyKxCfbv34/Q0FB07twZkiQhKSnJ3ClRG9NQHxJCYP78+ejUqRPs7OwQFBSEixcvmidZsljN0Y8KCwvx1ltvwcnJCc7Ozpg0aRJKS0tbsRVkTq3Vh06dOoXnn38etra28PDwQGxsbEs3jZoBi8QmKCsrQ+/evbF69Wpzp0JtVEN9KDY2FitXrkRcXBzS0tJgb2+P4OBglJeXt3KmZMmaox+99dZbOHv2LJKTk7Fz507s378f7777bms1gcysNfrQ7du3MWzYMHh5eSE9PR1LlixBTEwM1q1b1+Ltowdkvp+NfjgAEImJieZOg9qw+/uQVqsVGo1GLFmyRF5XXFwsVCqV+Oabb8yQIbUFTelH586dEwDEsWPH5Jjdu3cLSZLEjRs3Wi13sgwt1YfWrFkj2rdvLyoqKuSY2bNnCx8fnxZuET0ojiQSWZjs7Gzk5uYiKChIXteuXTv4+/vjyJEjZsyM2hJT+tGRI0fg7OwMPz8/OSYoKAgKhQJpaWmtnjNZlubqQ0eOHMGgQYOgVCrlmODgYGRlZaGoqKiVWkNNwSKRyMLk5uYCgN6vAbm5ucnbiBpiSj/Kzc2Fq6urznZra2t06NCBfY2arQ/l5uYaPEbdc5BlYpFIRERERHpYJBJZGI1GA6D2t0HrysvLk7cRNcSUfqTRaHDz5k2d7dXV1SgsLGRfo2brQxqNxuAx6p6DLBOLRCIL06VLF2g0GqSkpMjrbt++jbS0NAQEBJgxM2pLTOlHAQEBKC4uRnp6uhzzr3/9C1qtFv7+/q2eM1mW5upDAQEB2L9/P6qqquSY5ORk+Pj4oH379q3UGmoKa3Mn0BaVlpbi0qVL8nJ2djZOnDiBDh06wNPT04yZUVvRUB+aPn06Pv74Y3Tv3h1dunRBVFQUOnfujFGjRpkvabI4D9qPfH19ERISgoiICMTFxaGqqgrTpk3Dm2++ic6dO5upVdSaWqMPjR07FgsWLMCkSZMwe/ZsnDlzBitWrMCyZcvM0WRqDHPfXt0W/fzzzwKA3mv8+PHmTo3aiIb6kFarFVFRUcLNzU2oVCrx4osviqysLPMmTRanOfpRQUGBGDNmjHBwcBBOTk5i4sSJoqSkxAytIXNorT508uRJ8Yc//EGoVCrh7u4uFi9e3FpNpAcgCSFE65alRERERGTpOCeRiIiIiPSwSCQiIiIiPSwSiYiIiEgPi0QiIiIi0sMikYiIiIj0sEgkIiIiIj0sEomIiIhID4tEIrI43t7eWL58eb0xkiQhKSkJAHDlyhVIkoQTJ04AAFJTUyFJEoqLi1skv6ioKLz77rv1xgwZMgTTp09vkfMbMnDgQGzfvr3VzkdEDz8WiUTUJPn5+ZgyZQo8PT2hUqmg0WgQHByMQ4cOyTF1C7nmlpOTg+HDhxvcFhgYiJycHLRr1w4AkJCQAGdn52Y5b25uLlasWIG5c+c2y/Gay7x58zBnzhxotVpzp0JEDwkWiUTUJK+99hoyMjKwYcMGXLhwATt27MCQIUNQUFDQKufXaDRQqVQGtymVSmg0GkiS1Ozn/fLLLxEYGAgvL69mP/aDGD58OEpKSrB7925zp0JEDwkWiUTUaMXFxThw4AA++eQTvPDCC/Dy8sKAAQMQGRmJl19+GUDtJWMAeOWVVyBJkrx8+fJljBw5Em5ubnBwcMCzzz6Lffv26Z2jpKQEY8aMgb29Pdzd3bF69Wqd7fWNUta93JyamoqJEyfi1q1bkCQJkiQhJiYGH330EXr27Km3b58+fRAVFWW07Zs3b0ZoaKjOurKyMrz99ttwcHBAp06dsHTpUr39vvrqK/j5+cHR0REajQZjx47FzZs3AQBCCHTr1g2ffvqpzj4nTpyAJEm4dOkShBCIiYmRR247d+6Mv/zlL3KslZUVRowYgc2bNxvNnYioMVgkElGjOTg4wMHBAUlJSaioqDAYc+zYMQBAfHw8cnJy5OXS0lKMGDECKSkpyMjIQEhICEJDQ3H16lWd/ZcsWYLevXsjIyMDc+bMwfvvv4/k5ORG5xoYGIjly5fDyckJOTk5yMnJwcyZMxEeHo7MzEw5LwDIyMjAqVOnMHHiRIPHKiwsxLlz5+Dn56ezftasWfjll1/w/fffY+/evUhNTcXx48d1YqqqqrBw4UKcPHkSSUlJuHLlCiZMmACgtuANDw9HfHy8zj7x8fEYNGgQunXrhu3bt2PZsmVYu3YtLl68iKSkJPTq1UsnfsCAAThw4ECj3yMiIoMEEVETbNu2TbRv317Y2tqKwMBAERkZKU6ePKkTA0AkJiY2eKynn35arFq1Sl728vISISEhOjGjR48Ww4cPN3js7OxsAUBkZGQIIYT4+eefBQBRVFQkhBAiPj5etGvXTu+8w4cPF1OmTJGX33vvPTFkyBCjeWZkZAgA4urVq/K6kpISoVQqxZYtW+R1BQUFws7OTrz//vtGj3Xs2DEBQJSUlAghhLhx44awsrISaWlpQgghKisrhYuLi0hISBBCCLF06VLx5JNPisrKSqPH/P7774VCoRA1NTVGY4iITMWRRCJqktdeew3//ve/sWPHDoSEhCA1NRX9+vVDQkJCvfuVlpZi5syZ8PX1hbOzMxwcHJCZmak3khgQEKC3nJmZ2axtiIiIwDfffIPy8nJUVlZi06ZNCA8PNxp/9+5dAICtra287vLly6isrIS/v7+8rkOHDvDx8dHZNz09HaGhofD09ISjoyMGDx4MAHK7O3fujJdeegnr168HAPzwww+oqKhAWFgYACAsLAx3797FE088gYiICCQmJqK6ulrnHHZ2dtBqtUZHd4mIGoNFIhE1ma2tLYYOHYqoqCgcPnwYEyZMQHR0dL37zJw5E4mJiVi0aBEOHDiAEydOoFevXqisrGylrP8nNDQUKpUKiYmJ+OGHH1BVVYXXX3/daLyLiwsAoKioqFHnKSsrQ3BwMJycnLBx40YcO3YMiYmJAKDT7nfeeQebN2/G3bt3ER8fj9GjR0OtVgMAPDw8kJWVhTVr1sDOzg5Tp07FoEGDUFVVJe9fWFgIe3t72NnZNSo/IiJDWCQSUbPp0aMHysrK5GUbGxvU1NToxBw6dAgTJkzAK6+8gl69ekGj0eDKlSt6x/r111/1ln19fZuUl1Kp1MsDAKytrTF+/HjEx8cjPj4eb775Zr0FVteuXeHk5IRz587prLOxsUFaWpq8rqioCBcuXJCXz58/j4KCAixevBjPP/88nnrqKfmmlbpGjBgBe3t7fPHFF/jpp5/0RjXt7OwQGhqKlStXIjU1FUeOHMHp06fl7WfOnEHfvn1Ne1OIiBpgbe4EiKjtKSgoQFhYGMLDw/HMM8/A0dERv/32G2JjYzFy5Eg5ztvbGykpKXjuueegUqnQvn17dO/eHd999x1CQ0MhSRKioqIMPtvv0KFDiI2NxahRo5CcnIytW7di165dTcrX29sbpaWlSElJQe/evaFWq+URunfeeUcuPus+49EQhUKBoKAgHDx4EKNGjQJQexPPpEmTMGvWLHTs2BGurq6YO3cuFIr//Rvc09MTSqUSq1atwuTJk3HmzBksXLhQ7/hWVlaYMGECIiMj0b17d51L7gkJCaipqYG/vz/UajW+/vpr2NnZ6TyK58CBAxg2bFiT3iMiIj3mnhRJRG1PeXm5mDNnjujXr59o166dUKvVwsfHR8ybN0/cuXNHjtuxY4fo1q2bsLa2Fl5eXkKI2ptMXnjhBWFnZyc8PDzE559/LgYPHqxzk4eXl5dYsGCBCAsLE2q1Wmg0GrFixQqdHNCIG1eEEGLy5MmiY8eOAoCIjo7WOdbzzz8vnn76aZPa/uOPPwp3d3edm0NKSkrEuHHjhFqtFm5ubiI2NlavTZs2bRLe3t5CpVKJgIAAsWPHDp2c77l8+bIAIGJjY3XWJyYmCn9/f+Hk5CTs7e3FwIEDxb59++Tt169fFzY2NuLatWsmtYOIqCGSEEKYs0glIjInIQS6d++OqVOnYsaMGSbF+/v744MPPsCYMWOaPZ8DBw7gxRdfxLVr1+Dm5mbyfrNnz0ZRURHWrVvX7DkR0aOJcxKJ6JGVn5+Pzz//HLm5uUafjXg/SZKwbt06vTuLH1RFRQWuX7+OmJgYhIWFNapABABXV1eDl7CJiJqKI4lE9MiSJAkuLi5YsWIFxo4da9ZcEhISMGnSJPTp0wc7duyAu7u7WfMhImKRSERERER6eLmZiIiIiPSwSCQiIiIiPSwSiYiIiEgPi0QiIiIi0sMikYiIiIj0sEgkIiIiIj0sEomIiIhID4tEIiIiItLDIpGIiIiI9Pw/usTyFb+uD8wAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def to_percent(temp, position):\n",
    "    return '%1.0f' % (100 * temp) + '%'\n",
    "\n",
    "fig = plt.figure(1)\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax2 = ax1.twinx()\n",
    "lns = []\n",
    "\n",
    "stability_calibration = pd.DataFrame(columns=['stability', 'predicted_retention', 'actual_retention'])\n",
    "stability_calibration = dataset[['stability', 'p', 'y']].copy()\n",
    "stability_calibration['bin'] = stability_calibration['stability'].map(lambda x: math.pow(1.2, math.floor(math.log(x, 1.2))))\n",
    "stability_group = stability_calibration.groupby('bin').count()\n",
    "\n",
    "lns1 = ax1.bar(x=stability_group.index, height=stability_group['y'], width=stability_group.index / 5.5,\n",
    "                ec='k', lw=.2, label='Number of predictions', alpha=0.5)\n",
    "ax1.set_ylabel(\"Number of predictions\")\n",
    "ax1.set_xlabel(\"Stability (days)\")\n",
    "ax1.semilogx()\n",
    "lns.append(lns1)\n",
    "\n",
    "stability_group = stability_calibration.groupby(by='bin').agg('mean')\n",
    "lns2 = ax2.plot(stability_group['y'], label='Actual retention')\n",
    "lns3 = ax2.plot(stability_group['p'], label='Predicted retention')\n",
    "ax2.set_ylabel(\"Retention\")\n",
    "ax2.set_ylim(0, 1)\n",
    "lns.append(lns2[0])\n",
    "lns.append(lns3[0])\n",
    "\n",
    "labs = [l.get_label() for l in lns]\n",
    "ax2.legend(lns, labs, loc='lower right')\n",
    "plt.grid(linestyle='--')\n",
    "plt.gca().yaxis.set_major_formatter(ticker.FuncFormatter(to_percent))\n",
    "plt.gca().xaxis.set_major_formatter(ticker.FormatStrFormatter('%d'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(1)\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax2 = ax1.twinx()\n",
    "lns = []\n",
    "\n",
    "difficulty_calibration = pd.DataFrame(columns=['difficulty', 'predicted_retention', 'actual_retention'])\n",
    "difficulty_calibration = dataset[['difficulty', 'p', 'y']].copy()\n",
    "difficulty_calibration['bin'] = difficulty_calibration['difficulty'].map(round)\n",
    "difficulty_group = difficulty_calibration.groupby('bin').count()\n",
    "\n",
    "lns1 = ax1.bar(x=difficulty_group.index, height=difficulty_group['y'],\n",
    "                ec='k', lw=.2, label='Number of predictions', alpha=0.5)\n",
    "ax1.set_ylabel(\"Number of predictions\")\n",
    "ax1.set_xlabel(\"Difficulty\")\n",
    "lns.append(lns1)\n",
    "\n",
    "difficulty_group = difficulty_calibration.groupby(by='bin').agg('mean')\n",
    "lns2 = ax2.plot(difficulty_group['y'], label='Actual retention')\n",
    "lns3 = ax2.plot(difficulty_group['p'], label='Predicted retention')\n",
    "ax2.set_ylabel(\"Retention\")\n",
    "ax2.set_ylim(0, 1)\n",
    "lns.append(lns2[0])\n",
    "lns.append(lns3[0])\n",
    "\n",
    "labs = [l.get_label() for l in lns]\n",
    "ax2.legend(lns, labs, loc='lower right')\n",
    "plt.grid(linestyle='--')\n",
    "plt.gca().yaxis.set_major_formatter(ticker.FuncFormatter(to_percent))\n",
    "plt.gca().xaxis.set_major_formatter(ticker.FormatStrFormatter('%d'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  color: #000000;\n",
       "}\n",
       "#T_2ced5_row2_col9, #T_2ced5_row3_col11, #T_2ced5_row4_col2, #T_2ced5_row4_col3, #T_2ced5_row4_col7, #T_2ced5_row10_col5, #T_2ced5_row11_col2 {\n",
       "  background-color: #e5e5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_2ced5_row2_col10 {\n",
       "  background-color: #ffd1d1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_2ced5_row2_col11, #T_2ced5_row6_col0, #T_2ced5_row7_col6, #T_2ced5_row11_col6, #T_2ced5_row12_col1 {\n",
       "  background-color: #f1f1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_2ced5_row2_col12 {\n",
       "  background-color: #bdbdff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_2ced5_row3_col3, #T_2ced5_row5_col3, #T_2ced5_row8_col5 {\n",
       "  background-color: #d5d5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_2ced5_row3_col5 {\n",
       "  background-color: #b9b9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_2ced5_row3_col6 {\n",
       "  background-color: #c9c9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_2ced5_row3_col9, #T_2ced5_row4_col11, #T_2ced5_row7_col2 {\n",
       "  background-color: #f5f5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_2ced5_row3_col10, #T_2ced5_row9_col7, #T_2ced5_row11_col0 {\n",
       "  background-color: #fdfdff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_2ced5_row3_col12, #T_2ced5_row4_col6, #T_2ced5_row5_col8, #T_2ced5_row6_col9, #T_2ced5_row7_col10, #T_2ced5_row12_col0 {\n",
       "  background-color: #ffe9e9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_2ced5_row3_col13 {\n",
       "  background-color: #ff5d5d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_2ced5_row4_col4 {\n",
       "  background-color: #7171ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_2ced5_row4_col8, #T_2ced5_row6_col2, #T_2ced5_row11_col5, #T_2ced5_row13_col3 {\n",
       "  background-color: #c5c5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_2ced5_row4_col9, #T_2ced5_row5_col9, #T_2ced5_row5_col11, #T_2ced5_row6_col8 {\n",
       "  background-color: #f9f9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_2ced5_row4_col10, #T_2ced5_row5_col10, #T_2ced5_row8_col2 {\n",
       "  background-color: #cdcdff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_2ced5_row4_col12, #T_2ced5_row7_col7 {\n",
       "  background-color: #ffc5c5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_2ced5_row5_col2, #T_2ced5_row8_col3, #T_2ced5_row9_col5, #T_2ced5_row11_col1, #T_2ced5_row14_col1 {\n",
       "  background-color: #d9d9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_2ced5_row5_col6, #T_2ced5_row6_col4 {\n",
       "  background-color: #e9e9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_2ced5_row5_col7, #T_2ced5_row12_col2 {\n",
       "  background-color: #b1b1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_2ced5_row5_col12 {\n",
       "  background-color: #ffb1b1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_2ced5_row6_col3 {\n",
       "  background-color: #ffa1a1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_2ced5_row6_col5 {\n",
       "  background-color: #a1a1ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_2ced5_row6_col11 {\n",
       "  background-color: #ff0d0d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_2ced5_row7_col0 {\n",
       "  background-color: #fff5f5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_2ced5_row7_col3, #T_2ced5_row10_col2 {\n",
       "  background-color: #ededff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_2ced5_row7_col4, #T_2ced5_row10_col7 {\n",
       "  background-color: #fffdfd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_2ced5_row7_col5, #T_2ced5_row9_col4 {\n",
       "  background-color: #b5b5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_2ced5_row7_col9, #T_2ced5_row8_col8 {\n",
       "  background-color: #ff9d9d;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_2ced5_row8_col4, #T_2ced5_row16_col1 {\n",
       "  background-color: #ffeded;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_2ced5_row8_col6 {\n",
       "  background-color: #ff8d8d;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_2ced5_row8_col7 {\n",
       "  background-color: #ffc9c9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_2ced5_row8_col10 {\n",
       "  background-color: #ffcdcd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_2ced5_row9_col3, #T_2ced5_row13_col0 {\n",
       "  background-color: #ffb9b9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_2ced5_row9_col8 {\n",
       "  background-color: #ffadad;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_2ced5_row9_col9 {\n",
       "  background-color: #fa0000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_2ced5_row10_col3 {\n",
       "  background-color: #ffd9d9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_2ced5_row10_col4 {\n",
       "  background-color: #adadff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_2ced5_row10_col8 {\n",
       "  background-color: #ff6969;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_2ced5_row11_col3 {\n",
       "  background-color: #ff9999;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_2ced5_row11_col4 {\n",
       "  background-color: #1919ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_2ced5_row12_col3 {\n",
       "  background-color: #7979ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_2ced5_row12_col4 {\n",
       "  background-color: #5d5dff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_2ced5_row13_col2 {\n",
       "  background-color: #9d9dff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_2ced5_row14_col2 {\n",
       "  background-color: #a5a5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_2ced5_row15_col0 {\n",
       "  background-color: #ffa5a5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_2ced5_row15_col1 {\n",
       "  background-color: #c1c1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_2ced5\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >d_bin</th>\n",
       "      <th id=\"T_2ced5_level0_col0\" class=\"col_heading level0 col0\" >1</th>\n",
       "      <th id=\"T_2ced5_level0_col1\" class=\"col_heading level0 col1\" >2</th>\n",
       "      <th id=\"T_2ced5_level0_col2\" class=\"col_heading level0 col2\" >3</th>\n",
       "      <th id=\"T_2ced5_level0_col3\" class=\"col_heading level0 col3\" >5</th>\n",
       "      <th id=\"T_2ced5_level0_col4\" class=\"col_heading level0 col4\" >6</th>\n",
       "      <th id=\"T_2ced5_level0_col5\" class=\"col_heading level0 col5\" >7</th>\n",
       "      <th id=\"T_2ced5_level0_col6\" class=\"col_heading level0 col6\" >8</th>\n",
       "      <th id=\"T_2ced5_level0_col7\" class=\"col_heading level0 col7\" >9</th>\n",
       "      <th id=\"T_2ced5_level0_col8\" class=\"col_heading level0 col8\" >10</th>\n",
       "      <th id=\"T_2ced5_level0_col9\" class=\"col_heading level0 col9\" >11</th>\n",
       "      <th id=\"T_2ced5_level0_col10\" class=\"col_heading level0 col10\" >12</th>\n",
       "      <th id=\"T_2ced5_level0_col11\" class=\"col_heading level0 col11\" >13</th>\n",
       "      <th id=\"T_2ced5_level0_col12\" class=\"col_heading level0 col12\" >14</th>\n",
       "      <th id=\"T_2ced5_level0_col13\" class=\"col_heading level0 col13\" >15</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >s_bin</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "      <th class=\"blank col9\" >&nbsp;</th>\n",
       "      <th class=\"blank col10\" >&nbsp;</th>\n",
       "      <th class=\"blank col11\" >&nbsp;</th>\n",
       "      <th class=\"blank col12\" >&nbsp;</th>\n",
       "      <th class=\"blank col13\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_2ced5_level0_row0\" class=\"row_heading level0 row0\" >1.400000</th>\n",
       "      <td id=\"T_2ced5_row0_col0\" class=\"data row0 col0\" ></td>\n",
       "      <td id=\"T_2ced5_row0_col1\" class=\"data row0 col1\" ></td>\n",
       "      <td id=\"T_2ced5_row0_col2\" class=\"data row0 col2\" ></td>\n",
       "      <td id=\"T_2ced5_row0_col3\" class=\"data row0 col3\" ></td>\n",
       "      <td id=\"T_2ced5_row0_col4\" class=\"data row0 col4\" ></td>\n",
       "      <td id=\"T_2ced5_row0_col5\" class=\"data row0 col5\" >2.59%</td>\n",
       "      <td id=\"T_2ced5_row0_col6\" class=\"data row0 col6\" ></td>\n",
       "      <td id=\"T_2ced5_row0_col7\" class=\"data row0 col7\" ></td>\n",
       "      <td id=\"T_2ced5_row0_col8\" class=\"data row0 col8\" ></td>\n",
       "      <td id=\"T_2ced5_row0_col9\" class=\"data row0 col9\" >6.87%</td>\n",
       "      <td id=\"T_2ced5_row0_col10\" class=\"data row0 col10\" ></td>\n",
       "      <td id=\"T_2ced5_row0_col11\" class=\"data row0 col11\" ></td>\n",
       "      <td id=\"T_2ced5_row0_col12\" class=\"data row0 col12\" ></td>\n",
       "      <td id=\"T_2ced5_row0_col13\" class=\"data row0 col13\" >5.20%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ced5_level0_row1\" class=\"row_heading level0 row1\" >1.960000</th>\n",
       "      <td id=\"T_2ced5_row1_col0\" class=\"data row1 col0\" ></td>\n",
       "      <td id=\"T_2ced5_row1_col1\" class=\"data row1 col1\" ></td>\n",
       "      <td id=\"T_2ced5_row1_col2\" class=\"data row1 col2\" ></td>\n",
       "      <td id=\"T_2ced5_row1_col3\" class=\"data row1 col3\" ></td>\n",
       "      <td id=\"T_2ced5_row1_col4\" class=\"data row1 col4\" ></td>\n",
       "      <td id=\"T_2ced5_row1_col5\" class=\"data row1 col5\" >-5.15%</td>\n",
       "      <td id=\"T_2ced5_row1_col6\" class=\"data row1 col6\" ></td>\n",
       "      <td id=\"T_2ced5_row1_col7\" class=\"data row1 col7\" >4.93%</td>\n",
       "      <td id=\"T_2ced5_row1_col8\" class=\"data row1 col8\" >1.19%</td>\n",
       "      <td id=\"T_2ced5_row1_col9\" class=\"data row1 col9\" ></td>\n",
       "      <td id=\"T_2ced5_row1_col10\" class=\"data row1 col10\" >0.99%</td>\n",
       "      <td id=\"T_2ced5_row1_col11\" class=\"data row1 col11\" >1.36%</td>\n",
       "      <td id=\"T_2ced5_row1_col12\" class=\"data row1 col12\" >-1.33%</td>\n",
       "      <td id=\"T_2ced5_row1_col13\" class=\"data row1 col13\" >1.69%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ced5_level0_row2\" class=\"row_heading level0 row2\" >2.740000</th>\n",
       "      <td id=\"T_2ced5_row2_col0\" class=\"data row2 col0\" ></td>\n",
       "      <td id=\"T_2ced5_row2_col1\" class=\"data row2 col1\" ></td>\n",
       "      <td id=\"T_2ced5_row2_col2\" class=\"data row2 col2\" ></td>\n",
       "      <td id=\"T_2ced5_row2_col3\" class=\"data row2 col3\" >5.47%</td>\n",
       "      <td id=\"T_2ced5_row2_col4\" class=\"data row2 col4\" ></td>\n",
       "      <td id=\"T_2ced5_row2_col5\" class=\"data row2 col5\" >0.55%</td>\n",
       "      <td id=\"T_2ced5_row2_col6\" class=\"data row2 col6\" ></td>\n",
       "      <td id=\"T_2ced5_row2_col7\" class=\"data row2 col7\" >2.41%</td>\n",
       "      <td id=\"T_2ced5_row2_col8\" class=\"data row2 col8\" >0.19%</td>\n",
       "      <td id=\"T_2ced5_row2_col9\" class=\"data row2 col9\" >-0.96%</td>\n",
       "      <td id=\"T_2ced5_row2_col10\" class=\"data row2 col10\" >1.77%</td>\n",
       "      <td id=\"T_2ced5_row2_col11\" class=\"data row2 col11\" >-0.53%</td>\n",
       "      <td id=\"T_2ced5_row2_col12\" class=\"data row2 col12\" >-2.53%</td>\n",
       "      <td id=\"T_2ced5_row2_col13\" class=\"data row2 col13\" >2.50%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ced5_level0_row3\" class=\"row_heading level0 row3\" >3.840000</th>\n",
       "      <td id=\"T_2ced5_row3_col0\" class=\"data row3 col0\" ></td>\n",
       "      <td id=\"T_2ced5_row3_col1\" class=\"data row3 col1\" ></td>\n",
       "      <td id=\"T_2ced5_row3_col2\" class=\"data row3 col2\" ></td>\n",
       "      <td id=\"T_2ced5_row3_col3\" class=\"data row3 col3\" >-1.62%</td>\n",
       "      <td id=\"T_2ced5_row3_col4\" class=\"data row3 col4\" ></td>\n",
       "      <td id=\"T_2ced5_row3_col5\" class=\"data row3 col5\" >-2.69%</td>\n",
       "      <td id=\"T_2ced5_row3_col6\" class=\"data row3 col6\" >-2.04%</td>\n",
       "      <td id=\"T_2ced5_row3_col7\" class=\"data row3 col7\" >0.50%</td>\n",
       "      <td id=\"T_2ced5_row3_col8\" class=\"data row3 col8\" >0.20%</td>\n",
       "      <td id=\"T_2ced5_row3_col9\" class=\"data row3 col9\" >-0.37%</td>\n",
       "      <td id=\"T_2ced5_row3_col10\" class=\"data row3 col10\" >-0.09%</td>\n",
       "      <td id=\"T_2ced5_row3_col11\" class=\"data row3 col11\" >-0.98%</td>\n",
       "      <td id=\"T_2ced5_row3_col12\" class=\"data row3 col12\" >0.89%</td>\n",
       "      <td id=\"T_2ced5_row3_col13\" class=\"data row3 col13\" >6.32%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ced5_level0_row4\" class=\"row_heading level0 row4\" >5.380000</th>\n",
       "      <td id=\"T_2ced5_row4_col0\" class=\"data row4 col0\" ></td>\n",
       "      <td id=\"T_2ced5_row4_col1\" class=\"data row4 col1\" ></td>\n",
       "      <td id=\"T_2ced5_row4_col2\" class=\"data row4 col2\" >-1.02%</td>\n",
       "      <td id=\"T_2ced5_row4_col3\" class=\"data row4 col3\" >-0.96%</td>\n",
       "      <td id=\"T_2ced5_row4_col4\" class=\"data row4 col4\" >-5.48%</td>\n",
       "      <td id=\"T_2ced5_row4_col5\" class=\"data row4 col5\" >-1.25%</td>\n",
       "      <td id=\"T_2ced5_row4_col6\" class=\"data row4 col6\" >0.92%</td>\n",
       "      <td id=\"T_2ced5_row4_col7\" class=\"data row4 col7\" >-1.00%</td>\n",
       "      <td id=\"T_2ced5_row4_col8\" class=\"data row4 col8\" >-2.22%</td>\n",
       "      <td id=\"T_2ced5_row4_col9\" class=\"data row4 col9\" >-0.20%</td>\n",
       "      <td id=\"T_2ced5_row4_col10\" class=\"data row4 col10\" >-1.88%</td>\n",
       "      <td id=\"T_2ced5_row4_col11\" class=\"data row4 col11\" >-0.46%</td>\n",
       "      <td id=\"T_2ced5_row4_col12\" class=\"data row4 col12\" >2.21%</td>\n",
       "      <td id=\"T_2ced5_row4_col13\" class=\"data row4 col13\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ced5_level0_row5\" class=\"row_heading level0 row5\" >7.530000</th>\n",
       "      <td id=\"T_2ced5_row5_col0\" class=\"data row5 col0\" >-1.26%</td>\n",
       "      <td id=\"T_2ced5_row5_col1\" class=\"data row5 col1\" ></td>\n",
       "      <td id=\"T_2ced5_row5_col2\" class=\"data row5 col2\" >-1.50%</td>\n",
       "      <td id=\"T_2ced5_row5_col3\" class=\"data row5 col3\" >-1.70%</td>\n",
       "      <td id=\"T_2ced5_row5_col4\" class=\"data row5 col4\" >0.17%</td>\n",
       "      <td id=\"T_2ced5_row5_col5\" class=\"data row5 col5\" >0.17%</td>\n",
       "      <td id=\"T_2ced5_row5_col6\" class=\"data row5 col6\" >-0.82%</td>\n",
       "      <td id=\"T_2ced5_row5_col7\" class=\"data row5 col7\" >-3.09%</td>\n",
       "      <td id=\"T_2ced5_row5_col8\" class=\"data row5 col8\" >0.87%</td>\n",
       "      <td id=\"T_2ced5_row5_col9\" class=\"data row5 col9\" >-0.26%</td>\n",
       "      <td id=\"T_2ced5_row5_col10\" class=\"data row5 col10\" >-1.99%</td>\n",
       "      <td id=\"T_2ced5_row5_col11\" class=\"data row5 col11\" >-0.31%</td>\n",
       "      <td id=\"T_2ced5_row5_col12\" class=\"data row5 col12\" >3.05%</td>\n",
       "      <td id=\"T_2ced5_row5_col13\" class=\"data row5 col13\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ced5_level0_row6\" class=\"row_heading level0 row6\" >10.540000</th>\n",
       "      <td id=\"T_2ced5_row6_col0\" class=\"data row6 col0\" >-0.58%</td>\n",
       "      <td id=\"T_2ced5_row6_col1\" class=\"data row6 col1\" ></td>\n",
       "      <td id=\"T_2ced5_row6_col2\" class=\"data row6 col2\" >-2.29%</td>\n",
       "      <td id=\"T_2ced5_row6_col3\" class=\"data row6 col3\" >3.63%</td>\n",
       "      <td id=\"T_2ced5_row6_col4\" class=\"data row6 col4\" >-0.88%</td>\n",
       "      <td id=\"T_2ced5_row6_col5\" class=\"data row6 col5\" >-3.74%</td>\n",
       "      <td id=\"T_2ced5_row6_col6\" class=\"data row6 col6\" >0.95%</td>\n",
       "      <td id=\"T_2ced5_row6_col7\" class=\"data row6 col7\" >1.23%</td>\n",
       "      <td id=\"T_2ced5_row6_col8\" class=\"data row6 col8\" >-0.29%</td>\n",
       "      <td id=\"T_2ced5_row6_col9\" class=\"data row6 col9\" >0.87%</td>\n",
       "      <td id=\"T_2ced5_row6_col10\" class=\"data row6 col10\" >1.70%</td>\n",
       "      <td id=\"T_2ced5_row6_col11\" class=\"data row6 col11\" >9.38%</td>\n",
       "      <td id=\"T_2ced5_row6_col12\" class=\"data row6 col12\" ></td>\n",
       "      <td id=\"T_2ced5_row6_col13\" class=\"data row6 col13\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ced5_level0_row7\" class=\"row_heading level0 row7\" >14.760000</th>\n",
       "      <td id=\"T_2ced5_row7_col0\" class=\"data row7 col0\" >0.39%</td>\n",
       "      <td id=\"T_2ced5_row7_col1\" class=\"data row7 col1\" ></td>\n",
       "      <td id=\"T_2ced5_row7_col2\" class=\"data row7 col2\" >-0.38%</td>\n",
       "      <td id=\"T_2ced5_row7_col3\" class=\"data row7 col3\" >-0.76%</td>\n",
       "      <td id=\"T_2ced5_row7_col4\" class=\"data row7 col4\" >0.04%</td>\n",
       "      <td id=\"T_2ced5_row7_col5\" class=\"data row7 col5\" >-2.95%</td>\n",
       "      <td id=\"T_2ced5_row7_col6\" class=\"data row7 col6\" >-0.54%</td>\n",
       "      <td id=\"T_2ced5_row7_col7\" class=\"data row7 col7\" >2.34%</td>\n",
       "      <td id=\"T_2ced5_row7_col8\" class=\"data row7 col8\" >1.67%</td>\n",
       "      <td id=\"T_2ced5_row7_col9\" class=\"data row7 col9\" >3.90%</td>\n",
       "      <td id=\"T_2ced5_row7_col10\" class=\"data row7 col10\" >0.86%</td>\n",
       "      <td id=\"T_2ced5_row7_col11\" class=\"data row7 col11\" ></td>\n",
       "      <td id=\"T_2ced5_row7_col12\" class=\"data row7 col12\" ></td>\n",
       "      <td id=\"T_2ced5_row7_col13\" class=\"data row7 col13\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ced5_level0_row8\" class=\"row_heading level0 row8\" >20.660000</th>\n",
       "      <td id=\"T_2ced5_row8_col0\" class=\"data row8 col0\" >0.17%</td>\n",
       "      <td id=\"T_2ced5_row8_col1\" class=\"data row8 col1\" ></td>\n",
       "      <td id=\"T_2ced5_row8_col2\" class=\"data row8 col2\" >-1.95%</td>\n",
       "      <td id=\"T_2ced5_row8_col3\" class=\"data row8 col3\" >-1.44%</td>\n",
       "      <td id=\"T_2ced5_row8_col4\" class=\"data row8 col4\" >0.65%</td>\n",
       "      <td id=\"T_2ced5_row8_col5\" class=\"data row8 col5\" >-1.58%</td>\n",
       "      <td id=\"T_2ced5_row8_col6\" class=\"data row8 col6\" >4.40%</td>\n",
       "      <td id=\"T_2ced5_row8_col7\" class=\"data row8 col7\" >2.16%</td>\n",
       "      <td id=\"T_2ced5_row8_col8\" class=\"data row8 col8\" >3.85%</td>\n",
       "      <td id=\"T_2ced5_row8_col9\" class=\"data row8 col9\" >1.67%</td>\n",
       "      <td id=\"T_2ced5_row8_col10\" class=\"data row8 col10\" >2.01%</td>\n",
       "      <td id=\"T_2ced5_row8_col11\" class=\"data row8 col11\" ></td>\n",
       "      <td id=\"T_2ced5_row8_col12\" class=\"data row8 col12\" ></td>\n",
       "      <td id=\"T_2ced5_row8_col13\" class=\"data row8 col13\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ced5_level0_row9\" class=\"row_heading level0 row9\" >28.930000</th>\n",
       "      <td id=\"T_2ced5_row9_col0\" class=\"data row9 col0\" >0.23%</td>\n",
       "      <td id=\"T_2ced5_row9_col1\" class=\"data row9 col1\" ></td>\n",
       "      <td id=\"T_2ced5_row9_col2\" class=\"data row9 col2\" >0.99%</td>\n",
       "      <td id=\"T_2ced5_row9_col3\" class=\"data row9 col3\" >2.76%</td>\n",
       "      <td id=\"T_2ced5_row9_col4\" class=\"data row9 col4\" >-2.84%</td>\n",
       "      <td id=\"T_2ced5_row9_col5\" class=\"data row9 col5\" >-1.55%</td>\n",
       "      <td id=\"T_2ced5_row9_col6\" class=\"data row9 col6\" >1.39%</td>\n",
       "      <td id=\"T_2ced5_row9_col7\" class=\"data row9 col7\" >-0.13%</td>\n",
       "      <td id=\"T_2ced5_row9_col8\" class=\"data row9 col8\" >3.18%</td>\n",
       "      <td id=\"T_2ced5_row9_col9\" class=\"data row9 col9\" >10.46%</td>\n",
       "      <td id=\"T_2ced5_row9_col10\" class=\"data row9 col10\" ></td>\n",
       "      <td id=\"T_2ced5_row9_col11\" class=\"data row9 col11\" ></td>\n",
       "      <td id=\"T_2ced5_row9_col12\" class=\"data row9 col12\" ></td>\n",
       "      <td id=\"T_2ced5_row9_col13\" class=\"data row9 col13\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ced5_level0_row10\" class=\"row_heading level0 row10\" >40.500000</th>\n",
       "      <td id=\"T_2ced5_row10_col0\" class=\"data row10 col0\" >0.26%</td>\n",
       "      <td id=\"T_2ced5_row10_col1\" class=\"data row10 col1\" ></td>\n",
       "      <td id=\"T_2ced5_row10_col2\" class=\"data row10 col2\" >-0.71%</td>\n",
       "      <td id=\"T_2ced5_row10_col3\" class=\"data row10 col3\" >1.54%</td>\n",
       "      <td id=\"T_2ced5_row10_col4\" class=\"data row10 col4\" >-3.24%</td>\n",
       "      <td id=\"T_2ced5_row10_col5\" class=\"data row10 col5\" >-0.96%</td>\n",
       "      <td id=\"T_2ced5_row10_col6\" class=\"data row10 col6\" >0.57%</td>\n",
       "      <td id=\"T_2ced5_row10_col7\" class=\"data row10 col7\" >0.03%</td>\n",
       "      <td id=\"T_2ced5_row10_col8\" class=\"data row10 col8\" >5.88%</td>\n",
       "      <td id=\"T_2ced5_row10_col9\" class=\"data row10 col9\" ></td>\n",
       "      <td id=\"T_2ced5_row10_col10\" class=\"data row10 col10\" ></td>\n",
       "      <td id=\"T_2ced5_row10_col11\" class=\"data row10 col11\" ></td>\n",
       "      <td id=\"T_2ced5_row10_col12\" class=\"data row10 col12\" ></td>\n",
       "      <td id=\"T_2ced5_row10_col13\" class=\"data row10 col13\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ced5_level0_row11\" class=\"row_heading level0 row11\" >56.690000</th>\n",
       "      <td id=\"T_2ced5_row11_col0\" class=\"data row11 col0\" >-0.14%</td>\n",
       "      <td id=\"T_2ced5_row11_col1\" class=\"data row11 col1\" >-1.51%</td>\n",
       "      <td id=\"T_2ced5_row11_col2\" class=\"data row11 col2\" >-1.05%</td>\n",
       "      <td id=\"T_2ced5_row11_col3\" class=\"data row11 col3\" >3.98%</td>\n",
       "      <td id=\"T_2ced5_row11_col4\" class=\"data row11 col4\" >-9.04%</td>\n",
       "      <td id=\"T_2ced5_row11_col5\" class=\"data row11 col5\" >-2.27%</td>\n",
       "      <td id=\"T_2ced5_row11_col6\" class=\"data row11 col6\" >-0.56%</td>\n",
       "      <td id=\"T_2ced5_row11_col7\" class=\"data row11 col7\" ></td>\n",
       "      <td id=\"T_2ced5_row11_col8\" class=\"data row11 col8\" ></td>\n",
       "      <td id=\"T_2ced5_row11_col9\" class=\"data row11 col9\" ></td>\n",
       "      <td id=\"T_2ced5_row11_col10\" class=\"data row11 col10\" ></td>\n",
       "      <td id=\"T_2ced5_row11_col11\" class=\"data row11 col11\" ></td>\n",
       "      <td id=\"T_2ced5_row11_col12\" class=\"data row11 col12\" ></td>\n",
       "      <td id=\"T_2ced5_row11_col13\" class=\"data row11 col13\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ced5_level0_row12\" class=\"row_heading level0 row12\" >79.370000</th>\n",
       "      <td id=\"T_2ced5_row12_col0\" class=\"data row12 col0\" >0.89%</td>\n",
       "      <td id=\"T_2ced5_row12_col1\" class=\"data row12 col1\" >-0.49%</td>\n",
       "      <td id=\"T_2ced5_row12_col2\" class=\"data row12 col2\" >-2.99%</td>\n",
       "      <td id=\"T_2ced5_row12_col3\" class=\"data row12 col3\" >-5.23%</td>\n",
       "      <td id=\"T_2ced5_row12_col4\" class=\"data row12 col4\" >-6.39%</td>\n",
       "      <td id=\"T_2ced5_row12_col5\" class=\"data row12 col5\" ></td>\n",
       "      <td id=\"T_2ced5_row12_col6\" class=\"data row12 col6\" ></td>\n",
       "      <td id=\"T_2ced5_row12_col7\" class=\"data row12 col7\" ></td>\n",
       "      <td id=\"T_2ced5_row12_col8\" class=\"data row12 col8\" ></td>\n",
       "      <td id=\"T_2ced5_row12_col9\" class=\"data row12 col9\" ></td>\n",
       "      <td id=\"T_2ced5_row12_col10\" class=\"data row12 col10\" ></td>\n",
       "      <td id=\"T_2ced5_row12_col11\" class=\"data row12 col11\" ></td>\n",
       "      <td id=\"T_2ced5_row12_col12\" class=\"data row12 col12\" ></td>\n",
       "      <td id=\"T_2ced5_row12_col13\" class=\"data row12 col13\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ced5_level0_row13\" class=\"row_heading level0 row13\" >111.120000</th>\n",
       "      <td id=\"T_2ced5_row13_col0\" class=\"data row13 col0\" >2.71%</td>\n",
       "      <td id=\"T_2ced5_row13_col1\" class=\"data row13 col1\" >0.31%</td>\n",
       "      <td id=\"T_2ced5_row13_col2\" class=\"data row13 col2\" >-3.80%</td>\n",
       "      <td id=\"T_2ced5_row13_col3\" class=\"data row13 col3\" >-2.20%</td>\n",
       "      <td id=\"T_2ced5_row13_col4\" class=\"data row13 col4\" ></td>\n",
       "      <td id=\"T_2ced5_row13_col5\" class=\"data row13 col5\" ></td>\n",
       "      <td id=\"T_2ced5_row13_col6\" class=\"data row13 col6\" ></td>\n",
       "      <td id=\"T_2ced5_row13_col7\" class=\"data row13 col7\" ></td>\n",
       "      <td id=\"T_2ced5_row13_col8\" class=\"data row13 col8\" ></td>\n",
       "      <td id=\"T_2ced5_row13_col9\" class=\"data row13 col9\" ></td>\n",
       "      <td id=\"T_2ced5_row13_col10\" class=\"data row13 col10\" ></td>\n",
       "      <td id=\"T_2ced5_row13_col11\" class=\"data row13 col11\" ></td>\n",
       "      <td id=\"T_2ced5_row13_col12\" class=\"data row13 col12\" ></td>\n",
       "      <td id=\"T_2ced5_row13_col13\" class=\"data row13 col13\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ced5_level0_row14\" class=\"row_heading level0 row14\" >155.570000</th>\n",
       "      <td id=\"T_2ced5_row14_col0\" class=\"data row14 col0\" >2.48%</td>\n",
       "      <td id=\"T_2ced5_row14_col1\" class=\"data row14 col1\" >-1.47%</td>\n",
       "      <td id=\"T_2ced5_row14_col2\" class=\"data row14 col2\" >-3.49%</td>\n",
       "      <td id=\"T_2ced5_row14_col3\" class=\"data row14 col3\" ></td>\n",
       "      <td id=\"T_2ced5_row14_col4\" class=\"data row14 col4\" ></td>\n",
       "      <td id=\"T_2ced5_row14_col5\" class=\"data row14 col5\" ></td>\n",
       "      <td id=\"T_2ced5_row14_col6\" class=\"data row14 col6\" ></td>\n",
       "      <td id=\"T_2ced5_row14_col7\" class=\"data row14 col7\" ></td>\n",
       "      <td id=\"T_2ced5_row14_col8\" class=\"data row14 col8\" ></td>\n",
       "      <td id=\"T_2ced5_row14_col9\" class=\"data row14 col9\" ></td>\n",
       "      <td id=\"T_2ced5_row14_col10\" class=\"data row14 col10\" ></td>\n",
       "      <td id=\"T_2ced5_row14_col11\" class=\"data row14 col11\" ></td>\n",
       "      <td id=\"T_2ced5_row14_col12\" class=\"data row14 col12\" ></td>\n",
       "      <td id=\"T_2ced5_row14_col13\" class=\"data row14 col13\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ced5_level0_row15\" class=\"row_heading level0 row15\" >217.800000</th>\n",
       "      <td id=\"T_2ced5_row15_col0\" class=\"data row15 col0\" >3.50%</td>\n",
       "      <td id=\"T_2ced5_row15_col1\" class=\"data row15 col1\" >-2.41%</td>\n",
       "      <td id=\"T_2ced5_row15_col2\" class=\"data row15 col2\" ></td>\n",
       "      <td id=\"T_2ced5_row15_col3\" class=\"data row15 col3\" ></td>\n",
       "      <td id=\"T_2ced5_row15_col4\" class=\"data row15 col4\" ></td>\n",
       "      <td id=\"T_2ced5_row15_col5\" class=\"data row15 col5\" ></td>\n",
       "      <td id=\"T_2ced5_row15_col6\" class=\"data row15 col6\" ></td>\n",
       "      <td id=\"T_2ced5_row15_col7\" class=\"data row15 col7\" ></td>\n",
       "      <td id=\"T_2ced5_row15_col8\" class=\"data row15 col8\" ></td>\n",
       "      <td id=\"T_2ced5_row15_col9\" class=\"data row15 col9\" ></td>\n",
       "      <td id=\"T_2ced5_row15_col10\" class=\"data row15 col10\" ></td>\n",
       "      <td id=\"T_2ced5_row15_col11\" class=\"data row15 col11\" ></td>\n",
       "      <td id=\"T_2ced5_row15_col12\" class=\"data row15 col12\" ></td>\n",
       "      <td id=\"T_2ced5_row15_col13\" class=\"data row15 col13\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ced5_level0_row16\" class=\"row_heading level0 row16\" >304.910000</th>\n",
       "      <td id=\"T_2ced5_row16_col0\" class=\"data row16 col0\" ></td>\n",
       "      <td id=\"T_2ced5_row16_col1\" class=\"data row16 col1\" >0.69%</td>\n",
       "      <td id=\"T_2ced5_row16_col2\" class=\"data row16 col2\" ></td>\n",
       "      <td id=\"T_2ced5_row16_col3\" class=\"data row16 col3\" ></td>\n",
       "      <td id=\"T_2ced5_row16_col4\" class=\"data row16 col4\" ></td>\n",
       "      <td id=\"T_2ced5_row16_col5\" class=\"data row16 col5\" ></td>\n",
       "      <td id=\"T_2ced5_row16_col6\" class=\"data row16 col6\" ></td>\n",
       "      <td id=\"T_2ced5_row16_col7\" class=\"data row16 col7\" ></td>\n",
       "      <td id=\"T_2ced5_row16_col8\" class=\"data row16 col8\" ></td>\n",
       "      <td id=\"T_2ced5_row16_col9\" class=\"data row16 col9\" ></td>\n",
       "      <td id=\"T_2ced5_row16_col10\" class=\"data row16 col10\" ></td>\n",
       "      <td id=\"T_2ced5_row16_col11\" class=\"data row16 col11\" ></td>\n",
       "      <td id=\"T_2ced5_row16_col12\" class=\"data row16 col12\" ></td>\n",
       "      <td id=\"T_2ced5_row16_col13\" class=\"data row16 col13\" ></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2d0422b50>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B_W_Metric_raw = dataset[['difficulty', 'stability', 'p', 'y']].copy()\n",
    "B_W_Metric_raw['s_bin'] = B_W_Metric_raw['stability'].map(lambda x: round(math.pow(1.4, math.floor(math.log(x, 1.4))), 2))\n",
    "B_W_Metric_raw['d_bin'] = B_W_Metric_raw['difficulty'].map(lambda x: int(round(x)))\n",
    "B_W_Metric = B_W_Metric_raw.groupby(by=['s_bin', 'd_bin']).agg('mean').reset_index()\n",
    "B_W_Metric_count = B_W_Metric_raw.groupby(by=['s_bin', 'd_bin']).agg('count').reset_index()\n",
    "B_W_Metric['B-W'] = B_W_Metric['p'] - B_W_Metric['y']\n",
    "n = len(dataset)\n",
    "bins = len(B_W_Metric)\n",
    "B_W_Metric_pivot = B_W_Metric[B_W_Metric_count['p'] > max(50, n / (3 * bins))].pivot(index=\"s_bin\", columns='d_bin', values='B-W')\n",
    "B_W_Metric_pivot.apply(pd.to_numeric).style.background_gradient(cmap='seismic', axis=None, vmin=-0.2, vmax=0.2).format(\"{:.2%}\", na_rep='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_Metric:  0.039922575483365697\n",
      "R-squared: -6.8749\n",
      "RMSE: 0.1527\n",
      "[0.68927589 0.25115013]\n"
     ]
    },
    {
     "data": {
      "image/png": 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qFe+99x7r1q2jd+/eREVF0bJlS+zt7a3P6dix7P1JSzqfAtq0aWOzfeLECU6cOGETIiBJEmazmYsXL5b7OuzZsweNRsOBAwd47733WLx4sfWxiIgITpw4wYoVK4o85pkzZ1AqlbRufSOIv2HDhri7F67jduv5REREsHfvXhsLtslkIi8vj5ycHIYPH86nn35qPZ8BAwYwaNAglEplmc41KioKpVJp87719PQkODiYqKgo6z6NRmMTl1uaz8y9gr2jM44ubpUzmdkEexbCzvdAMoNXYxi+HHyblerpGbkGNp6MY/Wxqxy+lGbdr1ErKmd9gmIRAlBQo9CoNGhf01bbsUtLw4YNqV27Nv/++y9paWl069YNgICAAAIDA9m3bx///vsvDz30EGARAgAbNmygVq1aNnPZ2dlVaN0qlcpmWyaTlei+0Wq1DBo0iA8++MC6r6D/bKNGjbhw4UKF1nS7Y94aO1lAQSzdrFmz2LZtGx9//DENGzbEwcGBYcOGWRMVnJ2dOXbsGDt37mTr1q28/fbbzJkzh8OHD5c5+9jZ2ZlBgwYxaNAg3nnnHfr27cs777xTSKTfDrlcXihs4NaEh5LOpwBHR0ebba1Wy/jx43nxxRcLtYKrU6cOarW6XNehXr16uLm5ERwcTGJiIiNGjGD37t3WY/7vf/+zib27+Zhnzpwp8ZoUdz5z587l0UcfLTTW3t6ewMBAYmJi+Oeff9i2bRvPPfccH330Ebt27arU17yAoj4zt76WghLQJsLqyXBhp2U7dBQM+BjsCv+ovBmDycyes0n8eewa2yIT0Bst31UyGTzY0ItHW9eibzM/NGohT6oacYUFNQqZTIaj2rHkgTWAHj16sHPnTtLS0njppZes+7t27cqmTZs4dOgQzz77LABNmzbFzs6OK1euWMViSQQHB3P48GGbfbdulwa1Wl0o1q1169b8+eefBAUFoVRavgbMZjOZmZk4OjrSoEEDVCoVBw8epE6dOoAluP7MmTOlXv+ttG7dmvj4eJRKJUFBQUWO2bt3L+PHj7daPrVabaFEDqVSSa9evejVqxezZ8/Gzc2NHTt28OijjxZ5rqVBJpMREhLCvn37AGjSpAk//fQTeXl5Vivgzck9YIklzMrKIjs72yp4ChJEynI+RREWFkZMTAwNGza8bS/g4q5DaZgyZQoLFixgzZo1DB06lNatWxMZGUnDhg2LHB8cHIzRaOT48eM88MADAJw7d460tLQix99M69atredzOxwcHKyCfMqUKYSEhHDy5Elat25d6nNt0qQJRqORgwcP0qlTJwBSUlKIiYmhadOmpbouglJwYZdF/GkTQKWxCL+wMbcdLkkSp69nsvrYNdZFXCNZe+MHUGNfJx5tXZshrWrh52p/2zkElY8QgAJBOenRowdTpkzBYDDYiKJu3boxdepU9Hq9NQHE2dmZWbNmMWPGDMxmMw8++CAZGRns3bsXFxcXxo0bV2j+559/nq5du7Jw4UIGDRrEjh072LRpU5nLxAQFBXHw4EEuXbqEk5MTHh4eTJkyhW+//ZZRo0bx8ssv4+HhwZkzZ/j5559Zvnw5Tk5OTJo0iZdeeglPT098fHx44403bitGbsZkMhUSQnZ2dvTq1YuOHTsyZMgQPvzwQxo3bsz169fZsGEDQ4cOpU2bNjRq1IjVq1czaNAgZDIZb731lo01c/369Vy4cIGuXbvi7u7Oxo0bMZvNVtd0Ued665rDw8OZPXs2Tz75JE2bNkWtVrNr1y6WLl3KK6+8AsDo0aN54403mDx5Mq+99hqXLl3i448/tpmnffv2aDQaXn/9daZNm8bBgwdZvny5zZiSzud2vPzyy3Tq1Innn3+eyZMn4+joSGRkJNu2beOLL74o8TqUBo1Gw+TJk5k9ezZDhgzhlVdeoUOHDkydOpWnnnqq0DFDQkLo1asXTz/9NF9//TUqlYoXX3wRBweHEt+Tb7/9Ng8//DB16tRh2LBhyOVyIiIiOHXqFO+88w7Lly/HZDJZr+nPP/+Mg4MDdevWLdO5NmrUiMGDBzN58mS++eYbnJ2defXVV6lVqxaDBw8u9bUR3AazCXZ9ALs+BCTwbmJx+fqEWIdIkkSSVkd0XBbR8ZlEx2URfjWdC0k3ssm9nNQ8ElqLR1vXolmACzKZjHhtPJvPhRMRH0F4Qjh96vdhQtiEwmsQVBpCAAoE5aRHjx7k5uYSEhKCr6+vdX+3bt3IysqylospYP78+Xh7e7NgwQIuXLiAm5sbrVu35vXXXy9y/s6dO7N48WLmzp3Lm2++Sd++fZkxYwZffPFFmdY5a9Ysxo0bR9OmTcnNzeXixYsEBQWxd+9eXnnlFfr06YNOp6Nu3br06NHDKpg++ugjq6vY2dmZF198kYyMjBKPp9VqC8U1NmjQgHPnzrFx40beeOMNJkyYQFJSEn5+fnTt2tV6/RYuXMjEiRPp1KkTXl5evPLKK2RmZlrncXNzY/Xq1cyZM4e8vDwaNWrEL7/8QrNmzYo915upXbs2QUFBzJ07l0uXLiGTyazbM2bMAMDJyYm///6bZ555hrCwMJo2bcoHH3zAY489Zp3Hw8ODn3/+mZdeeolvv/2Wnj17MmfOHGsZl9Kcz+1o2bIl69evZ8GCBXTp0gVJkmjQoAEjRowo1XUoLVOnTmXhwoWsWrWKxx9/nF27dvHGG28UeUyAH3/8kUmTJtG1a1f8/PxYsGABp0+ftomVLIq+ffuyfv165s2bxwcffIBKpSIkJISnnnrKej7vv/8+M2fOxGQy0aJFC/7++288PT3LfK7Lli3jhRde4OGHH0av19O1a1c2btxYyO0rKCOZcRar36U9lu2wJ8nrvYBzaWaijsQSHX9D8KVkF64tqVbK6dPUlyFhAfh5pHEqKYKfo34k/N9wwuPDSchOsBkvQyYEYBUjk+6zwIerV68SGBhY5I1BcGfJysrizJkzNGnSpMxB/PcrkydPJjo6mj179lT63AUuYBcXl1JZ+u43Ll26RL169Th+/HiVt9+7W16Lgu/Tf/75h549e1b3cqqEnJwcoqKiaNy48V1fcDo1NZWv/j1XbBKI0WQmOTWVh1v4Y1Q7kqrVY39lJ91Pv4mjMY08mT1fOT3PGlMnrqXlYi5CQchlUM/LkYa+GlwdtZjkV0gzHyYy5SgnEk6Qa8wt9BwZMoK9gmnl14pWvq14sM6DdK7T2WZMwWcwNjaW2rVrV/Ry3PcIC6BAUIP5+OOP6d27N46OjmzatIkffviBr776qrqXJbhP2bFjB1qtlhYtWhAXF8fLL79MUFBQmWoQCqoXrc5I9OVUcvUmcgwmcvUmm38bzRKm3CxWHryC2kHDDOUfPKdYh1wmEWmuyxTDNC7m+gMWEeeuUdHE34VATwVquySypTPE5hziROJR/j1/FonCClGj0tDStyWtfFtZBJ9fK5r7NL9r4r/vFYQAFAhqMIcOHeLDDz8kKyuL+vXr89lnn1ndZgLBncZgMPD6669z4cIFnJ2d6dSpEytWrBDu1buE9Gw9q49dRa8sXmjJZRDimM0nDotobowE4IjXUI6EzOIpZyfyzEkk5l4gLucU0WmH2J4QTuL1osvo+Dn5EeobSphfmFXsNfRoiEIuSr1UN0IACgQ1mN9//726lyDIJygo6L4vFdK3b1/69u1b3csQlJNP/zlLrsGMu5OSBt5OOKgVaNQKHFQKm38brp3gudi5eBgzMKoc2N58CH/KzYRfnMjJxJPkGfMKzS2XyQn2DLaKvFZ+rQj1DcXXybeIlQhqAkIACgQCgUBwj3PoYiprwq8B0K+5H/6uDjc9KpGlyyJBewX7K4dwj78AqnSOOpgZYUjkfPg3NnM5qhxp6dvSYtnzD7O6cMtSS1VQ/QgBKBAIBALBPYzeaOb1NScBCPFzQqnM4mTCOeK18cRnxxOvjUelz2EYKmojJxv4Bh1z0OPlEsCA/MSMVn6tCPULpYF7A+HCvQcQAlAgEAgEgnuQLF0WJxJOsHjnRc4luiOT53A0ZQOHj9jWogyW5AxGjQMyDDI5Z72DeaTPSmJDuuDj6FNNqxdUNUIACgQCgUBwFyNJEteyrhEeH27zdz7tPEqzH/66L5EDKYqlKCQ/7BXO+Dr6EuDoS+vcDHzS8ntTO/ujajaYRnroUa8hHo4e1XpegqpFCECBQCAQCO4SDCYDMSkxhcReSm5K4cESBEgzkGGHn0cGsx98isiL/vj7BCHLTYfIvyAr3jI2sB3U6wZyBejT7+QpCaoJIQAFAoFAIKiBZOoyOZFwwkbonUo8hc6kKzRWIVMQ4hViScrIj9dLSKnN63+eQ62Qs3LCI7gp9FyLO4cs6QzEbASjDpT20GQgeDaqhjMUVCdCAAoE9yAymYw1a9YwZMiQKj3O8uXLmT59Ounp6QDMmTOHtWvXWnsBjx8/nvT0dNauXVul67iZoKAgpk+fzvTp0+/YMQWVi95oJiVbh5eTHSpFze2EUllIksTVzKs3hF6CpSfu+bTzRY53VjsT6hdqU0i5mU8z7JU3WvJl5BrouXIXAM/1aEB9bydSE67DhX8hPcYyyLUWNHkE7F2r/BwFNQ8hAAWCCrB//34efPBB+vXrx4YNG8r03OoWKvHx8bz77rts2LCBa9eu4ePjQ7NmzXjxxRfp3bt3ueacNWsWzz//fCWvtGhuFZ8FHD58GEdH0VHgbiZJqyNFq0NvNFPX8956LQ0mA1HJUYTHW0ReeIJF9KXmphY5vrZLbcL8wgj1DbWKvXru9ZDLihfGH22JJlmro76XI892bwAp5+HnMRDXABzsoE4HqNcFZCKb935FCECBoAJ8//33PP/883z//fdcv36dgICA6l5Sqbh06RKdO3fGzc2Njz76iBYtWqDT6Vi3bh3PP/880dHR5ZrXyckJJyenCq1Nr9ejVqvL/Xxvb+8KHV9Q/eQZTABk5hrRG82olXenFTAjL4OIhAgbF+7ppNPoTfpCY5VyJU28mhQqpOyp8SzzcY9fSWPFQUtixztDm2MXtQb+ng4ZmaBqBi2HgUeDip6e4C7n7vxUCQQ1AK1Wy2+//cazzz7LwIEDWb58eaExf//9N23btsXe3h4vLy+GDh0KQPfu3bl8+TIzZsxAJpMhk8kAiwu1VatWNnN8+umnBAUFWbcPHz5M79698fLywtXVlW7dunHs2LEyrf25555DJpNx6NAhHnvsMRo3bkyzZs2YMmUK+/bts45buHAhLVq0wNHRkcDAQJ577jm0Wu1t5y1q/QBz587F29sbFxcXnnnmGfT6GzfA7t27M3XqVKZPn46Xl5e100Rxx965cycTJkwgIyPDev3mzJkDWCyrn376qXX+K1euMHjwYJycnHBxceHxxx8nISGh0Jp/+ukngoKCcHV1ZeTIkWRlZZXpmgoqD53BUqZEQiI1u3C8W01DkiQup1/mr+i/mLtzLkN/G0q9RfVw+8CNbsu78cLmF1gWvozj8cfRm/S42LnQtW5XprWbxtJHlnLs6WNoX9Ny4tkT/Dj0R2Z2nMlD9R4ql/gzmsy8vuYUkgQjWnnRKXI+/DkJ9FlQqy20GiPEnwAQAlBQw5AkiRy9sVr+ytrm6/fffyckJITg4GCeeOIJli5dajPHhg0bGDp0KAMGDOD48eNs376ddu3aAbB69Wpq167NvHnziIuLIy4urtTHzcrKYty4cfz3338cOHCARo0aMWDAgFILltTUVDZv3syUKVOKdJW6ublZ/y2Xy/nss884ffo0P/zwAzt27ODll18u9VoBtm/fTlRUFDt37uSXX35h9erVzJ0712bMDz/8gFqtZu/evSxevLjEY3fq1IlPP/0UFxcX6/WbNWtWoWObzWYGDx5Mamoqu3btYtu2bVy4cIERI0bYjDt//jxr165l/fr1rF+/nl27dvH++++X6TwFlYPRZMZovlGnLjVbj9lcc1rw6U16wuPD+SH8B2ZsnkGPH3rg8aEHQYuCGPLbEObsmsPa6LVcSr8EQF3XujwS/Ahvd32b1Y+v5vy086S/ks6u8btY1H8RE8ImEOYfhp3SrlLWt2zvJaLiMmnlkMi7yS/A0eWADLq+BKN+AXXFLPR3G7t372bQoEEEBAQgk8kKxSMX/IC89e+jjz6yjgkKCir0+K3fDydOnKBLly7Y29sTGBjIhx9+WGgtq1atIiQkBHt7e1q0aMHGjRur5JxLi3ABC2oUuQYTTd/eUi3HjpzXF4269B+J77//nieeeAKAfv36kZGRwa5du+jevTsA7777LiNHjrQRO6GhoQB4eHigUChwdnbGz8+vTOt86KGHbLaXLFmCm5sbu3bt4uGHHy7x+efOnUOSJEJCQkoce3N8YlBQEO+88w7PPPMMX331VanXq1arWbp0KRqNhmbNmjFv3jxeeukl5s+fj1xu+Q3aqFGjQl+YxR1brVbj6uqKTCYr9vpt376dkydPcvHiRQIDAwH48ccfadasGYcPH6Zt27aARSguX74cZ2dnAJ588km2b9/Ou+++W+rzFFQOOqNF/KkUcmSA3mQmPVePh2PlCKSyYDQbyTHkkGvK5c1/32TX9V1EJkViMBtAAmQ3xirlSpp5N7Nx4bb0bYmHw52rpXctPZeF284wVL6HD+XLUSbngqM3PLoEGjwEqUXHGd7LZGdnExoaysSJE3n00UcLPX7rj+9NmzYxadIkHnvsMZv98+bNY/Lkydbtgu8KgMzMTPr06UOvXr1YvHgxJ0+eZOLEibi5ufH0008DsG/fPkaNGsWCBQt4+OGHWblyJUOGDOHYsWM0b968Mk+51AgBKBCUg5iYGA4dOsSaNWsAUCqVjBgxgu+//94qAMPDw22+MCqLhIQE3nzzTXbu3EliYiImk4mcnByuXLlSqueXxdL5zz//sGDBAqKjo8nMzMRoNJKXl0dOTg4aTen6foaGhtqM7dixI1qtltjYWOrWrQvAAw88UCXHjoqKIjAw0Cr+AJo2bYqbmxtRUVFWARgUFGTzhe7v709iYmKpjiGoXAri/+xVCpzsFMRl5JGs1eOuUVtDJSobSZLQm/QWsWfMJceQQ44hxxKrZwStSctfZ/7icvZlADwULXHLex1/z0zGd1XRplYrmng1qTQrXlnI1ZtIz9WTlm1g0abjzJW+4nH1LjABQV3gse/AuWw/Mu8l+vfvT//+/W/7+K0/IP/66y969OhB/fr1bfYX92N9xYoV6PV6li5dilqtplmzZoSHh7Nw4UKrAFy0aBH9+vXjpZdeAmD+/Pls27aNL774wur1uNMIASioUTioFETO61ttxy4t33//PUaj0SbpQ5Ik7Ozs+OKLL3B1dcXBwaGYGYpGLpcXEmgGg8Fme9y4caSkpLBo0SLq1q2LnZ0dHTt2tImrK45GjRohk8lKTPS4dOkSDz/8MM8++yzvvvsuHh4e/Pfff0yaNAm9Xl9qEVYabnVF38ljA6hUKpttmUyG2Wy+zWhBVVJgAbRTynHXqEnI1JFnMJGtM+FkX/Fbllkyk2fII8doEXm5BovgM0mmIser5CrUcjXPtXmOxn6NCfUN5eXf4jh8KY2rCU4cOOXLk6EtsFNWfjbtqWsZ/HcumfQcA+k5etJzDKTl6MnItfw/PcdgvV6NZFf5UrWIxsprSDI5sm6vQtdZlsLOglKRkJDAhg0b+OGHHwo99v777zN//nzq1KnD6NGjmTFjBkql5f24f/9+unbtapO81rdvXz744APS0tJwd3dn//79zJw502bOvn373tESWbciBKCgRiGTycrkhq0OjEYjP/74I5988gl9+vSxeWzIkCH88ssvPPPMM7Rs2ZLt27czYcKEIudRq9WYTLY3HW9vb+Lj45EkyWrtKKipV8DevXv56quvGDBgAACxsbEkJyeXev0eHh707duXL7/8kmnTphUSX+np6Xh4eHD06FHMZjOffPKJ1VX7+++/l/o4BURERJCbm2sVxAcOHMDJycnGKncrpTl2UdfvVpo0aUJsbCyxsbHW40VGRpKenk7Tpk3LfC6CqueGBVCOUiHHXaMiJVtPSrauzALQ6sI13LDq5RnzkChsBZchw15pj0atQaPUoFFpcFA5oM/TY0ww8uwDz+Ls7MzW0/EcvpRmzUz+JyqBZ38+xtdPtK5UEbjhRBwv/HocY4nxjxIjlbuYo1yOPXpy7LzRjFxmKfFyj5KVlUVmZqZ1287ODju7iltff/jhB5ydnQu5iqdNm0br1q3x8PBg3759vPbaa8TFxbFw4ULAUlKrXr16Ns/x9fW1Pubu7k58fLx1381j4uPjK7zu8lKz77QCQQ1k/fr1pKWlMWnSJFxdbQuoPvbYY3z//fc888wzzJ49m549e9KgQQNGjhyJ0Whk48aNvPLKK4DF7bh7925GjhyJnZ0dXl5edO/enaSkJD788EOGDRvG5s2b2bRpEy4uLtZjNGrUiJ9++ok2bdqQmZnJSy+9VGZr45dffknnzp1p164d8+bNo2XLluj1etavX8/y5cuJioqiYcOGGAwGPv/8cwYNGmSToFEW9Ho9kyZN4s033+TSpUvMnj2bqVOnWoVdUZTm2EFBQWi1WrZv3251M99qGezVqxctWrRgzJgxfPrppxiNRp577jm6detGmzZtynwugqrnhgXQIqY8nexIydaTmWtAbzShLkJk3ezCLfjLNeYWWW4FLF0zNKobIk+j0mCvtC+ytp6eG3MYTGbe32yxnD/1YD06NvDkqR+OsCM6kf/9dJTFTzyAfRk8Cbdj9bGrzFoVgVmCDvU9aOrvirtGhZtGhZtGjZtGhbtGjZtCj++e11CdXmV5YoOH0AxdAk73dimkW3+8zZ4921oFoCIsXbqUMWPGYG9vb7P/Zstdy5YtUavV/O9//2PBggWVIjyrC5EFLBCUke+//55evXoVEn9gEYBHjhzhxIkTdO/enVWrVrFu3TpatWrFQw89xKFDh6xj582bx6VLl2jQoIG1dl2TJk346quv+PLLLwkNDeXQoUOFslu///570tLSaN26NU8++STTpk3Dx8enTOdQv359jh07Ro8ePXjxxRdp3rw5ffv2ZdeuXXz55ZeAJXZv4cKFfPDBBzRv3pwVK1awYMGCsl4uevbsSaNGjejatSsjRozgkUceKfHLujTH7tSpE8888wwjRozA29u7yKw7mUzGX3/9hbu7O127dqVXr17Ur1+f3377rcznIah6TGYzBtMNFzAUxAIqkYCUbD1myUy2PpvknGSuZFwhOjma8PhwTiae5HzaeeK0cWToMqziz05hh5u9GwHOATT0aEgLnxa08mtFsFcwga6BeGm80Kg0JRZWBvj1cCwXkrLxcFTzTPcGdGnkzdLxbbFXydkZk8TTPx21WjDLy8qDV3gxX/yNaBPIiqc68PagpjzfsxFPdgxiUGgAXRp501wRS+1V/SziT6aAnm/DmD/vefEHFit+RkaG9e+1116r8Jx79uwhJiaGp556qsSx7du3x2g0cunSJcASR3hzaSnAul0QN3i7MWVNAqxMZFJZa1/c5Vy9epXAwEAuXrxoU1tNcOfJysrizJkzNGnSpNJjugRlx2w2k5mZiYuLS7HWOUHVc7++Fjl6I+cStSgVcpr6u2A0Gckx5pCeoyM9WwWYMcgvIVE4PlOGzGrNc1A6WK17SnnFHF05OTlERUVRO6gBA78+TLJWz9xHmjGuU5B1zL7zyUxafoRcg4kujbxY8mQbHNRltwQu23uRuX9HAjCuY11mD2qGXH5L4oskwdFlsOlVMOnAOQCGLYW6HUucPzU1la/+PYeji1ux47Iz03muR0M8PO5cBnNpuHTpEvXq1SM2NpbatWuX+fnFtcgcP348p06d4siRIyXOs2LFCsaOHUtycjLu7u58/fXXvPHGGyQkJFjjiV9//XVWr15tjbUeMWIEOTk5/P3339Z5OnXqRMuWLUUSiEAgENyPGExm5DIZiltv9PcRkiShM+lIzckFZJglHScSTti4cFXURYYKmeSMQpFtFXkFQu92LtzKYtXRayRr9dTzcmR0+zo2j3Vq4MWyCW2ZuPwwe84mM+mHw3w/rm2ZRODXO8/zQb57+X9d6/Nq/5DCWc95mfD3C3B6tWW7UR8Yshgcy14w+n5Bq9Vy7tw56/bFixcJDw/Hw8ODOnUsr2NmZiarVq3ik08+KfT8/fv3c/DgQXr06IGzszP79+9nxowZPPHEE7i7uwMwevRo5s6dy6RJk3jllVc4deoUixYt4v/+7/+s87zwwgt069aNTz75hIEDB/Lrr79y5MgRlixZUsVX4PYIASgQCATVhN5o5mxCFvZqBQ28q69Ab0q2joQMHUFemipPwjJLZmtSxs0lV8ySGYXkiQJ3DFIOpptcuBqVBsyQnQcahS/Bvs5VVhKm6DXDn8evAfBKv2BUisJCs0N9T36Y2I7xSw+x73wKE5YfYun4tiVeT0mS+L9/zvLZ9rMAvNCzEdN7NSp8ftfD4Y8JkHoB5EroORs6ToX7yEJcHo4cOUKPHj2s2wXxfOPGjbN2b/r111+RJIlRo0YVer6dnR2//vorc+bMQafTUa9ePWbMmGETF+jq6srWrVuZMmUKDzzwAF5eXrz99tvWEjBgsfatXLmSN998k9dff51GjRqxdu3aaqsBCEIACgQCQbWRmWfAJElk64yYzFK1WQHTsw0YzWZStHo0HpV3WzCYDDYiL9eQS64xt8ixMmQoZQ5IErjbO+Lp5IFGpUGRX8bEZDYTFZeF3mhGqzPibK8qcp6qINdkEett6rrTt9ntY7baBnnw46R2jFt6mAMXUhm/7DDLxrfF0a7oaypJEu9viuab3RcAeLlfMM91b3jrIDj0LWx9A0x6cA20uHwD21Xa+d3LdO/evcTap08//bSNWLuZ1q1bc+DAgRKP07JlS/bs2VPsmOHDhzN8+PAS57pTCAEoEAgE1URWntH6b53RVC0lkCRJIs9oSVzIzDNgliTkZbSuFbhwby25YjAbihyvlCttYvU0Kg12SjvOJGjRm814ObriZGcr8BRyOR6OapK1OlK0+jsmAPVGM3qT5Xq8NqBJiZbHB+rmi8DvD3HoYirjlx1i2YR2ON0iAs1miTl/n+bH/Zbi0rMHNWVCZ9tSIuSmw7qpEJUfNxY8AAZ/CZqaFZsnuDsRAlAgEAiqAbNZQqu7IQDzDNUjAA0mCVN+rTmT2WKNLE5cmc1mq1XvVhduURS4cG8uuaKSqwoJKbNZwlBQAuY2pVQ88wVgZp4BncF023GVSbJWhwQ82NCLB+q6l+o5reu489NT7Xny+4McvpTGuKWHWD6hrfW6mswSr68+yW9HYpHJ4N0hLQrFFXL1KPwxHtKvgFwFfeZD+2fgDrq+Bfc2QgAKBAJBNaDVGW1cU3mG6uk8cmvZkoxcg1WoGEwGi0XPmGNTSLkoZDIZGuUNkVfwb0UpO1HojGYkQCGXobyNK9xOpcDZXkVWnoGUbD0BbmXvtlMWsvIMZOtNyICJneqW6bmtAt1Y8VR7nvjuIEcvpzF26SF+mNgOjUrBrFURrA2/jlwGHw8P5dHWN2W0ShIc+Aq2zQazAdzqwvBlUKtwu0SBoCIIASgQCATVQFa+9U8pl2M0m8mtYP248lLg/lXKZRjNEuk5OnLM18gthQv3ZjeuvdK+QokZuvx12CsVxc7j5aQmK89AWrYeXxf7KoublCSJ+AyL2LVTSAS42ZfwjMK0rO3GyskdGPPdQY5fSefJ7w/h72LP5tPxKOUyFo0MY2BL/xtPyEmFv6ZAzEbLdpNH4JHPwcGtEs5IILBFCECBQCC4w0iSRFaeRVx5OqlJyMwjz2CyaQFYVZjMJvKMeVaLXlauGnBAJ6WgwA2zJCcrT4cks6zvZhdugRu3KBduRSmwgNqpis9qdbJTYqdUoDOaSMvR4+VUNZ0Y0nMM5BpMyOUyHCpwp2xey9ViCfz+IBGx6UQAaoWcr8a0plfTm1qDxR6CVRMg8yoo1ND3PWj7lHD5CqoMIQAFAoHgDqMzmtEbzchkMjwd1SRm6jCZJYwmCZWy8m74BpOhUKzerS5clbkOMgCZHrlch9nkgJvaH19XOxyUpXfhVpQCC2BJ/XRlMhmeTmqup+eSotXj6aiudDFqNkvEZ1quk4dGTUW7tTav5crKpzrwxPcHydEbWfJkG7o29i44GOz7DLbPA8kEHvVh+HLwD63gUQWC4hEFhASCGsacOXPw9fVFJpOxdu3a6l5Omdm5cycymYz09HQAli9fjpubm/XxOXPm0KpVqzu6pu7duzN9+vQ7esziKMj+dVQrUCrkVqtXrrF8bmBJksgz5JGam8rVzKucSTlDRHwEEQkRnE09y9XMq6TmplrFn1KuxMXOBV9HP+SoAWjq04hANy8ADEYVjirHOyb+AHT5FkD7EiyAAO4aNQqZDJ3RZHWlVybJWh0Gkxm1Qo6bQ+VkGzcNcGH3yz3Y92rPG+IvOwVWPg7/zLaIv+aPwdO7hPgT3BGEABQIysH48eORyWTIZDLUajUNGzZk3rx5GI0VuxlFRUUxd+5cvvnmG+Li4ujfv3+F11oWwZWZmcmbb75JSEgI9vb2+Pn50atXL1avXl1iLa3bMWLECM6cOVOu55aVW8VnAatXr2b+/Pl3ZA2locD9W5BsYZ9v9SpNH1mT2YRWryUxO5HL6ZeJSoriePxxTiWd4kLaBeK18WTqMq3xe/ZKe9zt3anlXItGHo0I9Q2llV8rGns2xkvjb0m8kMlQK+Q42ymRy2ToTeYK97QtC2ZJQndLD+DiUMhluDtahGuKVl/C6LJhNJlJytIB4OtqX6keWCc7JR756+byPlj8IJzbBkp7GLQIHvse7F0q74ACQTEIF7BAUE769evHsmXL0Ol0bNy4kSlTpqBSqcrVmNxkMiGTyTh//jwAgwcPvqOdDgDS09Pp27cvWq2Wd955h7Zt26JUKtm1axcvv/wyDz30kI0lr7Q4ODjg4FCxbE29Xo9arS7382tST1OTWSJbbxFXzvaWr2B7tRxyC2cCG81GcvU3FVI25t42C1cukxdqj1aSC1eXL/LsVYr8HzSWNWXkGsjINeJwh8rS6I1mpPz6g0V12SgKTydLSZisPAN5BhP2lVQSJiFLh0mScFApcHNQkZtbyRZGsxn++wT+fQ8kM3g2srh8/aqvI4Tg/kRYAAWCcmJnZ4efnx9169bl2WefpVevXqxbtw4AnU7HrFmzqFWrFo6OjrRv356dO3dan1vgFl23bh1NmzbFzs6OiRMnMmjQIADkcrmNAPzuu+9o0qQJ9vb2hISE8NVXX9ms5erVq4waNQoPDw8cHR1p06YNBw8eZPny5cydO5eIiAirxbKg/dGtvPHGG8TGxrJ//37GjRtH06ZNady4MZMnTyY8PBwnJ0ursp9++ok2bdrg7OyMn58fo0ePJjEx8bbX6VYXcAHffPMNgYGBaDQaHn/8cTIyMqyPjR8/niFDhvDuu+8SEBBAcHBwice+dOmSteWTu7s7MpmM8ePHA4VdwGlpaYwdOxZ3d3c0Gg39+/fn7Nmzhda8ZcsWmjRpgpOTE/369SMuLu6251lasvPLv6iVcqu1q8ACmK3TczXzKufSznEx9yInEk9wNvUs17KukZaXZhV/KrkKFzsX/Jz8qO9en+bezQnzC6OJdxPqutXF29EbJ7VTiS7cXKsAvHErcMl3eWbmFp0BXBXojDesf6X94WOnVOCSb0FNya4cK6DOYCI136Lo71qxrOYi0SbCz4/Cjncs4q/lSHh6pxB/gmpBWAAFNQtJAkNO9RxbpalQxp2DgwMpKSkATJ06lcjISH799VcCAgJYs2YN/fr14+TJkzRq1AiAnJwcPvjgA7777js8PT3x9/ene/fuTJgwwUZorFixgrfffpsvvviCsLAwjh8/zuTJk3F0dGTcuHFotVq6detGrVq1WLduHX5+fhw7dgyz2cyIESM4deoUmzdv5p9//gEsfStvxWw289tvvzFs2DACAgIKPV4g/gAMBgPz588nODiYxMREZs6cyfjx49m4cWOpr9W5c+f4/fff+fvvv8nMzGTSpEk899xzrFixwjpm+/btuLi4sG3btlIdOzAwkD///JPHHnuMmJgYXFxcbmt5HD9+PGfPnmXdunW4uLjwyiuvMGDAACIjI1GpVNbX5+OPP+ann35CLpfzxBNPMGvWLJs1lofMfPevUmHkSsaV/O4ZepTUxWCCeG0CcMPdbq+0L9Q1Q6WonLi0PGvc3Q2h6GyvRCaTkWc0VaplrThutkSWBS8nNZn5JWH8XOxQVLAvbnxmHhISzvYqnCq708iFXbB6MmgTQOkAAz+GVmNElq+g2hACUFCzMOTAe4UFyB3h9eugdizz0yRJYvv27WzZsoXnn3+eK1eusGzZMq5cuWIVU7NmzWLz5s0sW7aM9957D7CIma+++orQ0BsB3wWWMj+/G/1GZ8+ezSeffMKjjz4KQL169YiMjOSbb75h3LhxrFy5kqSkJA4fPmx1dTZseKOfqJOTE0ql0mbOW0lOTiYtLY3GjRuXeL4TJ060/rt+/fp89tlntG3bFq1WayMUiyMvL48ff/yRWrVqAfD5558zcOBAPvnkE+s6HR0d+e6772xcvyUdu+D8fXx8buuuLhB+e/fupVOnToBFZAcGBrJ6zRp6DhiMyWzGYDCwePFiGjRoAFhE/bx580p1fmB5XxjMBpvWaDmGXMwGP2QoydQnYDZk3/QMMyDHw94XJzs15jwzXm5eKBVV9zWdV4TwUsrlONkpycozkJlnuCMCMM9Y+vi/m3G0U2KvUpBnMJGabcDbufwlYbJ1RjJyDciwWP8qDbMJdn0Iuz4AJPAOgeE/gE9I5R1DICgHQgAKBOVk/fr1ODk5YTAYMJvNjB49mjlz5rBz505MJlMhMaXT6fD09LRuq9VqWrZsWewxsrOzOX/+PJMmTWLy5MnW/Uaj0WrJCw8PJywsrEJxbmVJ8Dh69Chz5swhIiKCtLQ0zGbLzfvKlSs0bdq0VHPUqVPHKv4AOnbsiNlsJiYmxioAW7RoUSjurzKOHRUVhVKppH379tZ9np6eBAcHczTiFE069SEt24BGo7GKPwB/f//burot/XTzCpVcMZpt48dkkhoVSkDCyV6Fo8rf0jlDqeFaupFsnRF3Ox9cHZRkGjKRy6ouSsdoNmMwFV17z8U+XwDmGvBxrkQxdBsKLIBlbe1WUEbnWnouKdk6vJzKVxImV2/kSqrF8+DuqK480ZsVD38+BZf2WLbDnoD+H4FaUznzCwQVQAhAQc1CpbFY4qqIrDwDdko56qJqjanK9qXco0cPvv76a9RqNQEBASiVlo+TVqtFoVBw9OhRFArb49xsIXNwcCjxZqXVagH49ttvbQQLYJ27ogkWAN7e3ri5uZWYrZudnU3fvn3p27cvK1aswNvbmytXrtC3b1/0+srNxnR0tLXG3oljF1jE8gwmqyu4AJlMhiRJmMwmG5FX0CrtdiK6wIWrUWkwGBxIy5ZwslNR37OhzTgHVS7ZOiN5RhOud+CruaDsilohR3mL69TFQcW19Fxy9Cb0RjPqMlrmyoIkSdYYQPtyHMddoyY+Mw+90UxWntEaw1haMnL0xKblYpYk7JQKfF0qSfCe3wGrn4bsJFA5wsP/B6EjKmdugaASEAJQULOQycrlhi0N2jwDFzMNqBXQ2FeDvIItpBwdHW1crQWEhYVhMplITEykS5cuFTqGr68vAQEBXLhwgTFjxhQ5pmXLlnz33XekpqYWaQVUq9WYTMWX9JDL5YwYMYKff/6Zd955h9q1a9s8rtVqsbe3Jzo6mpSUFN5//30CAwMBOHLkSJnP68qVK1y/ft3qIj9w4AByudya7FEUpTl2gcWwuPNt0qQJRqORgwcPWl3AKSkpxMTE8MQz0wFLBJ4E6E16q8iLz7KUAz4ef7zIeeUyeaFYPQelA/KbxNWFJC1gtCYv3ExBIkau/s6UXynK/VuASiHHUa0kW28kM89QZd02AAwmM+b8DijlEZpyuQwPRzVJWTqStbpSC0BJkkjM0pGQX/DZ2V5FoIdDITFcZkxG2LkA9nwCSODb3JLl69WoYvMKBJWMyAIW3Dck52f36U1mUnMq11p1M40bN2bMmDGMHTuW1atXc/HiRQ4dOsSCBQvYsGFDmeebO3cuCxYs4LPPPuPMmTOcPHmSZcuWsXDhQgBGjRqFn58fQ4YMYe/evVy4cIE///yT/fv3AxAUFMTFixcJDw8nOTkZnU5X5HHeeecdatWqRceOHfnxxx+JjIzk7NmzLF26lLCwMLRaLXXq1EGtVvP5559z4cIF1q1bV676evb29owbN46IiAj27NnDtGnTePzxx4uNUwwMDCzx2HXr1kUmk7F+/XqSkpKsFtSbadSoEYMHD2by5Mn8999/RERE8MQTT+DrH0D3PgMo+FkgSRInEk5wLvUc17KuoTXcmEslV+Fq54q/k78lC9fHkoUb4hVizcJ1VDvaiL+iyr/YXJN8IVYQD1fV5FndrkXfBgqEVEYVZwNbW8CVIQP4Vjwd1cgArc5YqvqFZrPEldQcq/jzcrIjyFNTcfGXcQ1+GAR7PgYkeGACPPWPEH+CGokQgIL7Aot76MaNrKD1VlWxbNkyxo4dy4svvkhwcDBDhgzh8OHD1KlTp8xzPfXUU3z33XcsW7aMFi1a0K1bN5YvX069evUAi9Vr69at+Pj4MGDAAFq0aMH7779vdRE/9thj9OvXjx49euDt7c0vv/xS5HE8PDzYunUrY8aM4Z133iEsLIwuXbrwyy+/8NFHH+Hq6oq3tzfLly9n1apVNG3alPfff5+PP/4YgKSsPJK1RYvLW2nYsCGPPvooAwYMoE+fPrRs2bJQaZubMZrNpJrs+fDzxUUeu4BatWoxd+5cXn31VXx9fZk6dWqhuUxmE58t/oxmoc0YMHAAHTp2ICMvg0XLf0SlUmGUpWBJyAC5ZKml5+HggaeDJX4z1DeUUL9QGnk2opZLLTwcPLBXllwyRHtT+ZeiLF0FLdCMJnOVvjcLyM0XXg63iXdzzW+Am6MzYjRVnSjVlTMB5GbUSoVVsJb0HtQbzZxP0loSPmQyartrCHArORyjRM5stRR2vrIP1M4wbCkM+hRUFQ/REAiqAplU3vL+dylXr14lMDCQixcvEhQUVN3Lua/JysrizJkzNGnSBI2maoOi4zPySMzKw1GtxGC29GH1c7W/IwHudwtms5nMzExcXFxsLFelIS1bT2xaDjKgib8LylIW8y0tmbkGLqVYMmYD3TXWLhDFIUmSpReu8aZYPUMOOlNRAkGB2mwR1C6OWRiNjuTo5LhpVNTxqJyQhKtpOaRm6/F0sqOWW9GiICY+C53RRD1PR0y67HK9FqVBkiQir2dikiQa+zrfNunhbEIWuQYTtd0d8HCsGjfw1dQcUnP0+LrYVyj+TqszciFJi1wmI8TPucj3YI7OyKXUHIwmM0q5nLqeGhztio+EysnJISoqisaNG+Ps7Fx4gMlg6eO77zPLtn8oDFsGng0Kj61mUlNT+erfczi6uBU7Ljszned6NKxRBdTBUuuzXr16xMbGFgpTEZQdEQMouOcxSxKp+YViPZ3USEBsag5JWTo8HdUVrh12v2OWJKsrTQKydEbcNeXv2lEUN7v14jLzcHFQobgphtMsmckz5tmUXMk15hbKwi1ArVDbxOrpDWoSMg04qBUEuQeSrTNyPklLZq4Rk9lc4feIJEnW/r/OxQgOe5UcndFEntFEJVehs8FgMmMqRdydq4OKXIOJjFxjlQnA8paAuRVHtcJaEiYtR4/3LT/u0nL0XE3LRZIk7FUKgjw1RSeDlYX0WPhjIlw9ZNlu9zT0eQeUVRczKRBUFkIACu55MnMNGM1mlAo5Lg4qZECSUkGe0USSVo9fZWX93SVI+Tf+yiI1W4/+JhdhVm7lC8DcmwSg0WTmWnoWdupcazZuriEXiaKdGQVCz0GVL/iUmkK19S5mW6yLrvnJGRq1AjulAp3RRHquAc8Kih+d0VJyRS6T4VSsAFSQkWsgz2DmNqF5lcLNcXfyYt4LLg4q4jPz0OoqRwjfiiUDuHwlYG5FJpPh5WTH1bQcUrR6vJzsrJnb8Zl51v6+LvYqAj00Nj8gykX0Rlj7LOSlg50rDP4cmg6u2JwCwR1ECEDBPU9BmygPR7X1ZufrYsfl1BxSsnR4Oaor3WVZU8nMNXAlNQcfZzt8KkH4mswSiZmWG6u7Rk1ajp4snaFSRKYkSehNenKNuWh1EiADuRbMTqTnmDDkxiPJbsR1KmSKGyIvPwPXQeVQYi09k9mMVmexzhXEkclkMjwcVcRlmEjLrrgALIg/dbRTFpt9bk0EMZioQE3jEikuA/jW9RQI4aw8I26VLOyNZgmTWUJGxS2AAG4OKuIz5OhNZjLzDDjZqYhNzbF2X/FxtsPXpYIt3ox6+Gc2HMiPWQ1oDcOXgXtQhdcvENxJqv2u9+WXXxIUFIS9vT3t27fn0KFDxY7/9NNPCQ4OxsHBgcDAQGbMmEFeXtHN0QWVS1aegRd/j2Dh1hhy9JXcIL2KyDOYyNYZkSHD46abl4uDCgeVApMkkVTKxIV7gaQsHeZ8i0haJfRPTdHqMJotdeJquTmglMtssl1Li1kyk2PIITknmdiMWGKSYwiPD+dk4knOpZ7HZLbcsPUkYSYHkGEv98ffyZ8G7g1o4dOCVn6tCPEKoY5rHbw0XpYs3FIUUs7KsyRn2CkVNoLITaNGhowcfekyS0s6BhSd/XszBaVg8oxmqjI6+0YLuJKvT0EySFVkAxcUgFYrFcVaIkuLpSSMRcQnZuosbvw8S7JHHQ8Nfq4VTPZIuwRL+94Qfx2mwMQtQvwJ7kqq1QL422+/MXPmTBYvXkz79u359NNP6du3LzExMfj4+BQav3LlSl599VWWLl1Kp06dOHPmDOPHj0cmk1lLYtRkJElizfFrNA1wIcTPpbqXU2bm/R3Jn8euAvDnsWu8PagpfZr6lvsLteB5VZmHVGD9c3FQ2sQ6yWQyfF3suZSSbXUXqe5xK6DOYCL7JuF+NT0XlVJerEuyOIwms1U8+7rYI5fLcLZXWayAeYbbzms0G2/E6hkt7tvbuXBlyLBTuGA2g1wGjT0boMCeC0m5mExqXNTuOFewZ2uBsHFxsF2vSiHH2V5p7TXrf5vEjZIwmc03yr+UcK3VCotL1ixJVGU1mNJaAMHyYykxS0dWnhGzWapw/UybdVRS/N/NeDjakZSlt4YNqBSWZA+Nunzv84LvJ/nFnbBhCugywN4NhnwNIQMqadUCwZ2nWgXgwoULmTx5MhMmTABg8eLFbNiwgaVLl/Lqq68WGr9v3z46d+7M6NGjAUt9s1GjRnHw4ME7uu7ysjMmiZm/R+DqoGLjC11umwlYE9l6Op5VR68ik4G/iz3X0nP5309HeSjEh7mPNCPQo+xZvEqlErPZTE5OTqGuD5WBySyRfpP791ac7ZVo1Epy9EYSs3R31etRHtLyax862SlRyGVk5Bq4nJJNQ2+ncsVfJWstpXTsVQrc8l2nzvZK0nL0ZOYa8XPJd+Hmd8ooSM7Qm4q2PCpkCttYPZUGe6U9aTkGrqXlolErcbGzdFLxdDKRrNURl56Ho6+y3NYjs/lGcoZrEQWEPRzVFgGYY8DX1b5cx9HqTFYLY0nXWSaTYa9SkKM3oq8iAWi26bxR8uvuoFKgUsgxmCyu8rJ22igOXQm1CMuDWinHVaMiPUePg0pBkKcjqgoITINBj8qQiXrLFNBnQO12lhIvboGVtmaBoDqoNgGo1+s5evQor732mnWfXC6nV69e1gK2t9KpUyd+/vlnDh06RLt27bhw4QIbN27kySefvO1xdDqdTeHbrKwsAAwGAwZD1RY4vZV14RbrWUaugRd+OcZPE9rcFbFnKVodr64+AcDkB4OY2r0BX++6wHd7L7EjOpG955J5rlt9Jj0YVKZf8mazmaysLJKSkgDQaDSVmpyQkWvEaNChVshRmA3k5BR+vd3tJLJz9KRkGHBWmFEqKu/4NY2UjBwksxknjRxHOyW5eSZ0ehMX4g3UdndALrN8LnNzc0t8HYxmicR0S/szd0d7cnJz0Bl15Bp0SEYFeUYIj72IiaLFnkquwk5hh4PKATulHQ4KB1SKW4SFAfIMeWi1OiSjAaXaUpIDwFklkWo2kJunJz7VbBWgZSVbb8Rk0FkKABv15Bht16sA5GYjBqOZlHRKLBlSFGmZlvXbq1TW9ReH0mxAMhrIg1K9FmVFZzRjNuqQy2UY9XkYSxEJoJGbSNcZSMmQUEqVF5yYnZuLZDQhM8nJyak8T4CnHTjI5WhUCgz6PAzljHYwG3UkXTmLa9x/KPWZmDo+j7nb66BQwR2+f1QGBoMBSTJhNhcf0iBJpmq5R5ZETVvP3U61CcDk5GRMJhO+vr42+319fYmOji7yOaNHjyY5OZkHH3wQSZIwGo0888wzvP7667c9zoIFC5g7d26h/bt37yYyMrJiJ1EGjGbYfFIByJDLJI5cTmf6d1sZEHhnqv6XF0mCpWfkpGbL8ddIBOvP8e8/5wgBXmoBqy7IOZsJ/7f9HCv2nmVYfTPBrmX/Is/Ozq70emeZehlGCTRKiZzE24/LMoDBLCM9XsLxHk2LMpghyyBDLgNjiuX1MUuWfSYJEq5ZRFVpkJDINkjozQpkMiOJ8ZmYpBuuZbnkhgw1ZlkWErkoZcobf3LL/435/2WTXeLxsgwyDGbIUUok3WSw0pkg2ygjWQauaonyyKRsI+hMMuwVEnlJRY/JNUKuyfL+cCqHzkzXyzBLkKuSSC7FW7zgvNLlEukJZT9eSehNoDXKUMlBn1S619xohkyD5VqnqitPqBVcm7xkCWUN++2lMOtQG7NQ5KbgefZ3DtSfSWJeKGzZVt1LKzdZWVmcvybH3rGIeoY3kZedxba880XXPaxGkpOTq3sJ9xR31e1u586dvPfee3z11Ve0b9+ec+fO8cILLzB//nzeeuutIp/z2muvMXPmTOv2tWvXaNq0KV27dr2jhaB3nkki9+BxfJzteKVvY1784yRbr8l5sk872terWcU2b2bN8eucOHAKlULG4vEdaOpvG7s4XpL4+0Q8CzbHkKjV81Wkgodb+PFa/2B8SkhjNBgMbNu2jQ4dOiCXyzEajZUWD3j6eiZv/nEStVLOyklti41zi4rLYsaqE8jlMr59IuyedAUv2HSGXWcTeaSlP891r2/dfyklm5mrTpGjN9Ij2IsO9gl07twJpVKJJElc014jJjmGmJQYolOiOZNyhoRMHT76OchQkqRaiF5+BgAXOxeCPYPRGB7i3NV6BPup+HhYO1Ty8rsMJUli2JJDZOuMfD26FfW8boQKmCWJKb9EcDE5m4Et/Hi+R9kK75rMEqO+O0RmnpH3H21G49puRY6LTctl8k/HkMng5wlt8XQqfSbsxeRs3l4Zjlop54+n25eq1+2pa5nM+fMkLiqJlZM7oFRW7tf0d/9d4o9j1xjU0p8pN70XisMsWa5VRq6RBUObERboVuF1ZOYZmLbEkvS35pkOOKgrWJOvsjDqkO/7FEXUWjCbUHgGsaPeTLoMHIFKVZXVGaue1NRULu65gMbZrdhxOVnp9O5Sv0YWghZUHtUmAL28vFAoFCQk2P7ETUhIuG0/0Lfeeosnn3ySp556CoAWLVqQnZ3N008/zRtvvFGkBcnOzg47uxtCJDMzEwCVSnVHP8xbIy3mhX7N/XisTR0OXkrj9yNXefGPk2x6oWuRMWrVzfX0XOZvsFhjp/dqTGgdzyLHPdamDr2a+bNwaww/HbjM+pPx7DqTzIt9GvNkx6AS621VxWvxy/FzXMsy8Xgbf/y93Isd287ZmeBa19gRncjXe6+xaGRYpa6lusnIMfDHiUT0RjMDWwfZ/Kpv4ezMW4MVTFh+mJ+PJHDY8yxbZJs5kXSCiPgIMnQZhebz1M/AaJLh7HyFJ9o/SKjvFFr5taKOax1kMhnnk7T0/GQXSTkSanuXcieZgKVg95lkHSqFjJBAn0IC6vneTRmx5ADf7b/GkDb1aRpQ+uSq/edTiErS4aZR0bFxrduGYzR1dsbPw4Wjl9PYFJPOs91LLzT3Hk3kWpaJHsEeeLq7luo5Tevacy3LxDXArLDD2blyu+SEx+VyLctEoI9bmSw8Lev68OvhWLadyaBr04rHv0WnpHIty0QtNwd8PN0qPF+lkHwWVo2HhFOADLq8iOHBWeRt3nrH7xlVgUqlQiZTIJeXFIuqqJHnW9PWc7dTbQFoarWaBx54gO3bt1v3mc1mtm/fTseOHYt8Tk5OTiGRV9DvtCZ3tDOYzGyNtAjd/s39AZjzSDMaeDuSkKnjpVURNW79ZrPES39EkKUzElbHjf91Ld5S4OqgYu7g5vw15UFCa7uSpTMy5+9IHvniP8Jj0+/MovNJ1urYdDIegCc7BJXqOTN7NwZgXcR1YuKzqmpp1cK6iGvojWZC/JxpFuBCSk4KOy7uYOH+hYxdM5ap23uSrPwSgJiURiw9cILdl3eToctAJVcR5hfG+FbjWdRvESse2YGzqScAP499nLe7vc3gkMHUdatrjVWr7+VIkKcGvcnMf2cr5rKJzn8tGvo4F2k9a1/fk4Et/TFLMPfv02X6HG05bXmP9AzxLTEWd0Qbi+BZdSS2TMfYGWOJPegRUriqwe1wdVAR4Gqp0RiToC3180pLwfu7rJUI+jaz/DDfGhmPuRJ6FZ9LtJxbAx+nCs9VKUT8Bt90s4g/R294cjX0fAvkd5WjTCAoNdWagTBz5ky+/fZbfvjhB6Kionj22WfJzs62ZgWPHTvWJklk0KBBfP311/z6669cvHiRbdu28dZbbzFo0CCrEKyJ7D+fQkauAS8nNe3y3b0atZLPR7VGrZSzPTqRZXsvVe8ib+HH/ZfYey4FB5WChY+3KnWySovarqx+rjPvDGmOi72S09czGfrVXqasPMZ/Z5Mr5cZREr8fiUVvMhMa6EaL2qWzujSv5cqAFn5IEnyyNaaKV3hnMEtmzqeeZ8l/JwHIkG+m7qd18frIi54/9uTFrS/y04mfOJl4kkzFRnR2GwHwNc5iXucVRDwTgfZ1Lcf+d4xlg5cxrf00dp50RgL6NfMj9DZuQJlMZhU8O6IrFsQWFWex2Dfxv72l6rX+Idgp5Ry8mMqmU/GlmleSJLbl/yjr28y3hNEwoKU/GrWCC8nZHLmcVqpjZOYZOJo/tnvj0gtAgGA/iyiKSajcHyPpOXri89v2BfuVLb6rU0NPnOyUJGTqCL+aXuG1nM0Xt42qWwDqc2DtFFjzNBiyIagLPPMfNHioetclEFQx1frTZsSIESQlJfH2228THx9Pq1at2Lx5szUx5MqVKzYWvzfffBOZTMabb77JtWvX8Pb2ZtCgQbz77rvVdQqlYtOpOMDyC/pmd2jTABfeHNiEt/86zYJNUbSr50HzWqUTLFXJ+SQtCzZZXL+vDwixibsqDQq5jCc61KVvMz8WbIpi9bFrbDgRx4YTcdR2d+DxNoEMe6A23lWQcWEyS6w4cAWAJ9rXKdNzZ/ZuzOZT8WyNTODE1XRa3iYmrCaSZ8zjdOJpwuPDLX8J4UTER5CX50GA7kskjBxK/RKzzCKo6rvXp5VfK1r5trL8368VXnY+PL5oKyfT5KzZ783YsAaoFTdCE8Jj09kamYBcBi/2aVzsenqG+LJs7yV2RCdVqHZcgQC8Nfb0Zmq7a3imWwMWbT/LuxuieCjEp8T6dqeuZXItPRcHlYKujb1LXIeTnZKBLfxZdfQqvx+OpW1QybFRe88mYzRL1Pd2pI5n2dy4Ib7O/BuTTHR85VoACyyqtd0dyuyat1MqeCjEh3UR19lyKp7WdYoPrSiJc0mWc2tYnQIwMcri8k2KBmTQ/VXo+hKU4CIVCO4Fqt22PXXqVKZOnVrkYzt37rTZViqVzJ49m9mzZ9+BlVUORpOZLactloYBLfwLPf5kh7r8dzaZrZEJPP/Lcf5+/sEKxUyBxX17LklLfS/HMpeZMZrMzPw9Ap3RTJdGXjzRoW651+HtbMfCx1sxsXM9fjscy9rwa1xNy2XhtjP83z9n6NLQkwYyGb2MZiortGNnTCLX0nNxdVAxKDSgTM9t6OPMkFa1WH38Gh9vPcOPE9tVzqIqmeScZCLiI6xCLzw+nKikKExS4dIOnuZRAPh4JDCr2zu08mtFS9+WuNoX/qFhMBh4spGZH6+6cep6JhOWH2bNs51x1VhenI+2WH4UDA2rTSPf4q1H7ep54KhWkKzVcep6RrnFdIEALMld+Uy3Bqw6Esu19Fy+2XWBF3o1KnZ8gfu3W2PvUhVDBhjRNpBVR6+y4WQcsx9pVuLndGeMJe63rNY/uGGdq2wLYHndvwX0a+7HuojrbD4dz6v9QypUouZc/rlViwVQkiB8BWyYBcZccPKFx76Del3v/FoEgmqi2gXgvc7Bi6mkZutx16iKzPaVyWR8OKwlpxbtsWQM/nWKhY+3KvfxouIyeWvtKY5cTiO0tiufjQqjrmfpLXhf7zxPRGw6zvZKPhzWslJqkDWv5UrzWq68PqAJm0/H8dvhWA5cSGX32RR2o+Cvj3fxaFhtRrQNLFFYlMTPBy4D8Hib2qW+sd/MC70asS7iOrvPJHHoYqrVZV8dmCUzF9IuEB5vseYViL2rmVeLHO/p4Gm15rXya0Vz71AmfhdHqsHAgocH06tpya5OOwV880QYw785yIWkbJ75+Sg/TGzH4Uup7D2XgkohY3oJ4gosxXi7NvZm06l4tkcllksAZuuMXE611M0rzgUM4KBW8NqAJjz/y3G+3nWO4W1qE1BMNneBAOzbvORrUsADdd2p7+XIheRsNpy4zoi2t7cwS5LEzjOW+L/uwSVbGG8l2Nciis4kaCu1+0Z0fIGgLt/nrFtjb9RKOZdTcohJyCq3kNTqjFzPsLii77gFUKeFDS/CiV8t2/V7wKNLwKnsQl0guJsRArCK2Xjyhvv3dtY4N42aT0eGMXLJflYfu8aDDb14tHXtMh1HqzPyf9vOsHzfJUz5cXYRVzMY+Nl/vDu0OYNb1SpxjlPXMli0/SwA8wc3x9+1csuhOKgVDA2rzdCw2lxKzubXQ5dZuf8CqdkGvvvvIt/9d5HWddx4tX+TcgmvKyk57DxjsbqMaV8+y2VdT0eGtwnkl0NX+HhrDL893aHSC/EWRa4hl9NJN7lw48OJSIhAqy/aBdjQoyGhvqE2gq+Wcy2btW45HU9q9hW8nOzKJEJ8nO34fnxbhi/ez/4LKbyx5iRn8gP2x7SvW+quLw+F+LDpVDw7ohOZ0bt4l3FRxCRkIUmW9Xg6lVx8+OGW/vy0/zKHLqWyYFM0n48qOpv7fJKWs4lalHIZD4WUXgDKZDKGtwnkg83R/H7karECMCoui4RMHQ4qRbney0GeGpQyiRy9idi0nDL9iCuOAhdwWeP/CnC0U9K1kTf/RCWw+VR8uQXg+fz3k5eTHW6aO1gBIf6UxeWbchZkcujxBjw4Eyq5BqlAcDcgBGAVYjJLVktD/yLcvzfTrp4H03s1ZuG2M7y59hStAt2o713yL2NJkthwMo756yNJyLR0POnf3I+nutTng03RHLqUygu/hrPnbDJzH2l2204GeQYTM34Lx2iWGNDCj8GtyuY+LStBXo682LsRjfVncWzYlj+OXWdHdCLHrqQz+tsDzBvcnNFljOFbcegykgRdGnkRVMa4xZuZ1rMhfx67yqGLqfx3LpkujcpuwSmOpOwkm1i98PhwYpJjinTh2insaOHbwiZWr4VvC1zsSr7xrjpisRQ+1vr2JU5uRxN/Fz4fHcak5YdZddQyj4NKwZQeDUs9R/dgi0Xl5LUMEjLz8HWxL9MarO7fYuL/bkYmk/H2oKYM+uI//o64zpMd6hYpvgo+kx0beBbZ/q04Hmtdi4+3xnD0chrnErNo6FO0kPo3P/u3UwPPclmilQo5fhq4mm0Rk5UhAM1miTNWF3D5Le39mvtZBeD0XmUX9nAjA7ihT+W3gCwSSYKjy2Hzq2DMA+cAGPY91O10Z44vENRAhACsQg5fSiVZq8fVQUWnBkXX0LuZKT0asu98MgcupPL8L8dZ/Vwn7Irp1XkhScvsdafZk19qo66nhjmPNKNH/o135eT2fLbjHF/sOMsfR69y7HIan48Oo1lA4fivhdvOcDZRi5eTHe8MaXFHrF4AChk8FOxN3+YBJGblMX99FH9HXOf1NSc5k5DFmwOblEq85BlM/H44FrDEVVYEf1cHxrSvw7K9l/h4SwwPNvQq1/UwS2bOpZ4r5MK9nnW9yPFeGq9CiRnBXsEoy1GGIilLZxUhw9uUzZpcQI9gS5/nt/46DcDEB4PwLqG49814O9sRGuhGRGw6/0YnMrJd2QR9aTKAb6V5LVdGtg3kl0OxzP37NOumPlioDmVBTG5BWZOy4ONiT49gb/6JSmTVkau8NqBJkeN2FcT/laH8y60EaCSuZsuIjs+kX/Oyr/VWrqblkq03oVbIy5zYdTO9mvigkMuIjs/iUnJ2uX5sFSSANLqNgK5U8jJh/XQ49adlu2FvGPoNOJb8nSwQ3Mvc13ZvSZLYHpVARhE9YiuDTfnu3z5NfVGVQsQo5DI+HRGGu0bF6euZfLCp6HIkeQYTC7fG0O/TPew5m4xaKWd6r0Zsmd7VKv7AYkWY2bsxKyd3wM/FngvJ2Qz9ch/L9l60qWV28EIK3+65AMD7j7aotqLUPs72fDayFbPyM0yX77vEhOWHS/X6bDoVR1qOgQBXex6qwE23gOe6N8RBpSDiaoa1XEhx5BhyOHTtEEuOLuG5Dc/R6ftOuCxwIfiLYEb8MYL3/nuPjWc3WsVfI49GDG86nHcfepcNozdwbeY1Emclsu3JbXzU5yPGtBxDM59m5RJ/AGuPX8NklmgV6HZbK1VpeLJjEC/3C6ZPU1/+161snTYAelrLwRTTi+82RMdZrFXFZQAXxaw+wTjnlyBadSTW5rH4jDwiYtORySyfy/IwPL8m4J/HrmEwFW7lmJFr4OiVgvIv5bceB2gsn9GC61BRCuL/Gvo4VagHuZtGTYf6FstqgTW1rBSUgKny+L+4CFjSzSL+ZAroPQ9G/y7E333E7t27GTRoEAEBAchkMtauXWvz+Pjx45HJZDZ//fr1sxmTmprKmDFjcHFxwc3NjUmTJqHV2obnnDhxgi5dumBvb09gYCAffvhhobWsWrWKkJAQ7O3tadGiBRs3bqz08y0L97UF8K2/TvHzgSs89WA93ny4aaXObTZL1ppkRWX/3g4/V3s+Hh7KpB+OsHTvRTo39KRnkxs3qh3RCcxed5rY1FzAEpQ995Fmxf4K71Dfk00vdOGlPyL4JyqRuX9HsvdcMh8OC0WtlPPiqggkyVLstjSJAlWJTCZj6kONaOjjzIzfLK7roV/t5btxbYp1if+035L8Mbp9nQrd3ArwdrZjfOcgvt55noXbztCria81ED8xO9EmVi88PpyYlBjMUmExYK+0p4VPC5tYvRY+LXC2qzrLhyRJ/J4vfMpr/buZ57qX3u17Kw+F+LBw2xn+O5dMnsFUaneo2SxZ49WalFEAejrZMb1XY+avj+SjLTH0b+FvdfVujbR8JsMC3fApo0u6gIdCfPByUpOs1fFvdCJ9brEk/nc2GZNZoqGPU6njJYsiIP8jXSDcKkpMJbh/C+jXzI+951LYcjq+XD8MzlstgFUkACUJDn8HW14Hkx5cA2HYUgismZn9gqojOzub0NBQJk6cyKOPPlrkmH79+rFs2TLr9s3dwwDGjBlDXFwc27Ztw2AwMGHCBJ5++mlWrlwJWDqM9enTh169erF48WJOnjzJxIkTcXNz4+mnnwZg3759jBo1igULFvDwww+zcuVKhgwZwrFjx2jevHkVnX3x3NcCsFcTX34+cIXl+y4xun2dUsXclZajV9JIzNLhbK+kU8Oy/drs2cSXCZ2DWLb3ErNWRbDpha4YzWbm/R1p7Sji72rP7EFN6dvMr1TuSXdHNd+ObcMP+y7x3sZo/olKpP+i3TTxd+FqWi613Bx48+Gi3VnVQb/mfgR6dGTyD0e4kJzNkC/38uWY1kXG4526lsGxK+ko5TIeb1vxFlUFPPVgXX7Yd5Ho+Cye+OX/SOcfwuPDidPGFTneW+NNmH+YTXJGY8/G5bbilZeIqxmcTdRip5SXuRROZdMswAVfFzsSMnUcvJhKt1JaxK6m5aLVGcvtrhzbsS4rD17mfFI2n28/a/2BZ83+LYf7twCVQs6jrWuzZPcFfj9ytZAALOj+URHrH0CtfAvg5dQcsnXG28bvlpYCQR1SBpf67ejTzI+3/jrNsSvpZY7vzDOYuJySDVSRBTA3Hf6eBpF/WbaDB8DgL0FTs/raCu4M/fv3p3///sWOsbOzu20L2qioKDZv3szhw4dp06YNAJ9//jkDBgzg448/JiAggBUrVqDX61m6dClqtZpmzZoRHh7OwoULrQJw0aJF9OvXj5deegmA+fPns23bNr744gsWL15ciWdceu5rF3D3YB+6B3tjNEu8tzG6UucuyP7t3cS32Di+2/Fq/xCaBbiQlmNgzHcH6L1wN1sjE1DKZfyva33+mdmNfs39yxSbJpPJGN+5HmundKZ+fhu6nTFJyGTwyeOhONvXrD6LzQJc+WvqgzxQ153MPCPjlx1m+S3ua4AVBy3Wv37N/fBxLp9VJ8eQw8GrB/nmyDc8u/5ZOn7fkTqfeXHd/CMAu045svXMEeKy4pEho7FnYx5v9jjvPfQeG0dv5PrM6yTMSmDLE1v4sPeHjG4xmqbeTe+4+AOsbs9+zf1wqebXVCaTWV3yO6JK3xUkMj/+r5GvU6nCJ25FpZDz9qBmgCWU4FyilvQcPQcupAIVE4AAwx+wWFb/jUkkMSvPut9slqyZ6N2DKxaK4KQCbyc1kgRnKqEeYIElMbicmbs34+tiT+s6bgBsLYMb2Gy2dGAxS+BiryxTTGmpuHYUvulqEX9yFfRdACNXCvF3j5GVlUVmZqb1T6fTVWi+nTt34uPjQ3BwMM8++ywpKSnWx/bv34+bm5tV/AH06tULuVzOwYMHrWO6du2KWn0jfKpv377ExMSQlpZmHdOrVy+b4/bt25f9+/dXaO0V4b4WgABvDmyCQi7jn6gE9p6rWN/SAsxmic2nSpf9ezvslAo+HxWGRq3gfFI2uQYT7YI82DCtC68NaFIha0DTABfWP/8gj+e7B6f2aEiH+jUzJsbb2Y6Vk9vzWOvamMwSc/6O5PU1p9AbLe7WzDwDa49b4upKm/wRr41n87nNvP/f+4z8YyQhX4TgvMCZDt934JkNz7D46GIOXD1AjiEHg/1WZPIcVFJtauuW0cj4N71dd9DL4xfau86nqfNYajt0wlntfccSZ4ojz2BiXYTlegx/oPKsoRWhoNTK9ujEUvfRLRArZXX/3ky3xt70DPHBaJZ4Z0Mk/0QlYjJLBPs6VyhLHKCRrzNhddwwmSVWH7tm3R8Zl0lSlg6NWkHbehXrlAE3yrVEV7A/dZ7BxKUUS03FynABww0RvbkUAjAuI5fPt5+l+8c7ef6X44Dle6jSPjOSBPu/gu/7QvplcKsDk7ZAx+egBnwuBZVL06ZNcXV1tf4tWLCg3HP169ePH3/8ke3bt/PBBx+wa9cu+vfvj8lkqcoQHx+Pj4/tjzmlUomHhwfx8fHWMQUdzAoo2C5pTMHj1cF97QIGS/eHJ9rX4Yf9l5m/PpIN07oUyhosK+FX04nLyMPJTkmXRl7lnqe+txOfjQxjye4LjGwXyNCwWpX2halRK/lwWCizB92+NExNwU6p4OPhLQn2c2LBpmh+OXSFC0lavn7iAdaFXyPXYKKxr1Ohkh8ms4kzKWeISIiwiddLyC7aEuXn5GfNwg31s7hxG3k0YsvpRD7ZGsPllBz0RomY+Gxi4rMLPd/XxY76Xk408HGkvpcT9b0t//d2tsNeJb8jAnHL6Xiy8ozUcnMoVeb5naBzQ0/USjlX03I5l6gtVbHvGxnAFbNWvflwU3afTWJnTJI18aBvJWTUgiVm9viVdH4/Esv/utZHJpNZ3b+dGniVy/J/K8G+Tvx3LsUav1deziVqMZkl3DUqfCrJ6mZp9RjNgQuppOfoC9Xz0xvN/BOVwO9HYtl9JomCNuDOdkoeaRXAM+WIHSySnFT4awrE5AfUNxkEj3wBDm6VM7+gxhEZGUmtWjdq294as1cWRo4caf13ixYtaNmyJQ0aNGDnzp307NmzQuus6dTsO/8dYnqvxqw5fo3o+Cx+PxLLqDKWq7iVguzfnk1K7klaEr2a+lZpYkZNF38FyGQynu7agIY+Tkz7JZyDF1MZ/OV/yLCIquFt/Dhw9YBNfb2TCSfJNeYWmksuk9PYs7FNyZVQv1D8nIoWBgNa+DOghT8Gk5nY1BwuJGVzIVlr+X/+v5O1ehIydSRk6th/IaXQHGqFHFeNCjcHFW4aFa4OKlwd1NZ/3/i/2vL//H0u9qoydYH4I79m32MP1K607hEVRaNW0qmBJztjktgenVhKAZifAFJBa1U9L0cmdq7HN7svcC3d8l7o26xyPk8DW/oz9+9ILiRlc+xKGg/U9bC2f+sRUjm1IwusdQWCuLzcXAC6sn6IBHk5EuLnTHR8Fv9EJTIs3y1+JiGL3w7Hsub4NVKz9dbx7et5MKJtIP2b++OgrqReu7GH4I+JkBELCjX0fQ/aPiWsfvc4zs7OuLhUPJShKOrXr4+Xlxfnzp2jZ8+e+Pn5kZhoW8XAaDSSmppqjRv08/MjIcHWsFCwXdKY28Ue3gnujrt/FePuqGZ6r8bMWx/Jx1tiGNjSv9yxU5IksfFkvvu3efncv4Lb0yPYh2/GNeKFX6KsmdDIdDyzvTVmWU6h8RqVxiYpI9Q3lBa+LdCoyp6dqVLIqe/tlJ8sZCsiMnIMXEjWcj4pmwtJWqswvJSSg95oRm8yk5SlIymrbLEq9b0dWTqubalcltfSc/kvP4yhIEatptAzxIedMUnsiEos0fKTlWfgirUFXMW/5Kc+1JA/j10jWaujtrtDmcvK3A5nexUDWvjz57Gr/HY4lobezhwrKP9Swfi/AoJ9b7iAJUkqt3iLsbaAq9ybZr/mfkTHZ/FXuKUkzm+HYwmPTbc+7uNsx7AHavN4m8AKu91tMJth/+ewfR6YjeBeD4Yvh4BWlXcMwX3J1atXSUlJwd/fcv/u2LEj6enpHD16lAceeACAHTt2YDabad++vXXMG2+8gcFgQJXf2H7btm0EBwfj7u5uHbN9+3amT59uPda2bdvo2LHjHTw7W4QAzOfJjnX5+eBlLiRl8+W/53itf/kyYk9czeBaei4ataJcPUAFNzCajZxJOVOo5EpSThJyyQVv+WvYm1uQKd+CWZaDv5O/TbmVVn6taODeAIW8kqwNxeCqURFWx52wOrZxX5JkaeeVnmsgI8dAeq4+//8G0nMMZOQayMjVk55z87aB9Bw92XoTF5KyGfXtAX59ukOJ3SD+PHoVSYIO9T0qVH6kKugR4gN/nebI5aLdhTdTkPDg52KPeyXUpHS2VzF7UFNe+PU4Y9rXrVRX/Ii2gfx57CrrT8TxQF13zJKltEmtYvoQl4X63o4o5TIycg3EZ+aVuz1jdCWWgLmZfs39+PSfs+w5m2wtSK+Uy+jZxIfH2wTSrbF3pZRlsiE7BdY+A2e3WrabPQqDFoF91ViEBHc3Wq2Wc+fOWbcvXrxIeHg4Hh4eeHh4MHfuXB577DH8/Pw4f/48L7/8Mg0bNqRv374ANGnShH79+jF58mQWL16MwWBg6tSpjBw5koAAS5WF0aNHM3fuXCZNmsQrr7zCqVOnWLRoEf/3f/9nPe4LL7xAt27d+OSTTxg4cCC//vorR44cYcmSJXf2gtyEEID5qBRy3hjQhEk/HGHZf5cY064udTzLfhPdeMri/u0RUnH37/2EVq/lRMIJG6F3MvEkeca8QmPlMjnBXrVo6XMSX7UjfUIepU2tefg6VW8Nw6KQyWQ42ilxtFOWWRQkZuUx+tuDnEvUMmrJAX59uuNt35Nms2R1/9aU5I+bqe2uIdjXmZiELHadSSq2N3Vkgfu3EsqVFDAoNICeTXxwqOTPZNsgd4I8NVxKyeH9TZZKAj0qoRB5AXZKOQ28nYhJyCI6LqvCArC8PYBvR7CvM80CXDh9PZMG3o6MaBvI0LDalZ/dW8DlffDHJMi6Dgo76P8BPDBeuHwFt+XIkSP06NHDuj1z5kwAxo0bx9dff82JEyf44YcfSE9PJyAggD59+jB//nybuMIVK1YwdepUevbsiVwu57HHHuOzzz6zPu7q6srWrVuZMmUKDzzwAF5eXrz99tvWEjAAnTp1YuXKlbz55pu8/vrrNGrUiLVr11ZbDUAQAtCGh0J86NLIiz1nk1mwKYqvn3igTM+XJIlN+e7fAcL9WySSJBGnjSM8Ppyj146y+dJmZi2exfnU80gUzhB1VDnS0reljVWvuU/zcrlw7zZ8nO1ZObk9o5Yc4PxNlsCirHuHLqVyJTUHJzsl/VtUX0xJcTzUxIeYhCx2RCcWKwDL2gO4tGjUlf91J5PJGN4mkI+2xJCW37GmovX/biXE3yKco+IzyyUuU7P11tCDxqWIvywLMpmMFU+1JzFLRyMfp6pLdDKb4b+F8O97IJnAs5HF5etXfTdPwd1B9+7di60+sGXLlhLn8PDwsBZ9vh0tW7Zkz549xY4ZPnw4w4cPL/F4dwohAG9CJpPx5sCm9F+0m02n4jlwIaVM5VFOX8/kSmoO9ip5pQWB380YzUZikmOsFr2CbNyknKQixwc4BxTqhdvAowFy2f1brcjH2Z5fJndg5LcHuJCUzcglRYvAVUcs1r+BLfyrROhUBj1DfPh653l2xiRhNJlv6xqsrAzgO8WwB2rzydYYzBI4qhW0CarcmnMhfi78xfVyt4QrKKlT11NTJUlfbhp1sS79CqNNgjVPw/kdlu2WI2DgQrCr4jZyAsE9Ts28U1QjwX7OjGpXhxUHrzB/fWSRzeRvx6YC92+wT429CVcVWbosWxdufhauzlQ46UEhUxDiFUJLn5ao09Q83vVx2tRug49j5bnO7iV8XOz5dXIHRi45wIXkG5bA2u4WEajVGa2Fxyuj9VtVEVbHHTeNivQcA8eupBcq2wMWV3ZByZOmlegCrkp8XezpHuzDjuhEOjf0Qq2s3B8sBZ07ytsSrkA4Bley9e+OcHE3/PkUaBNA6QADP4ZWY4TLVyCoBO4vlVJKZvZuzLrw65y+nsmfx67yeJuSY6pssn/LWfz5bkCSJK5lXSuUmHE+7XyR453UTjZZuK38WtHMuxkOKgcMBgMbN26kd/3e1swpQdH4uNjzy9MWEXjRKgI7UsvNgY0n4sg1mKjv5cgDdStefLiqUMhl9Aj2Yc3xa2yPTihSAF5JzSFHb0KtlBNUQtJLTeLlfsEYzRLPP9So0udukp+5ez4pG53RVOb6gpXZA/iOYTbB7o9g1wcgmcE7xOLy9ak57SoFgrsdIQCLwNPJjmk9G/Huxig+2hLDgBb+OJXgOomOz+JicjZqpdza+upux2AyEJ0cbeO+DY8PJyW3cJ07gNoutW0KKYf5hVHPvd597cKtTHxd8t3BS/ZzKSUnPzGkA6uOWlq/DWtTu0Z0IymOHiEWAbgjKrHITPsC92+wr3PlZ49WISF+Lvw4sV2VzO3rYme1nJ5L1NIswLVMz49OKOgBfHe41MmKh9WTLdY/gFZPwIAPQX33/CAQCO4GhAC8DWM7WcrCXE7JYfHO88zqG1zs+ILiz90ae5coFmsiGXkZhVy4pxJPoTfpC41VyBQ09W5q6ZZxUyFlL035u54ISoef6w1L4OWUHIZ9vY/rGXnIZfBY65rr/i2gWyNvFHIZZxO1xKbmFIplvBH/dxdZq6oYmUxGiJ8zBy6kEh2XVSYBaDSZOVNFGcBVwvkdsPppyE4ClSM8vBBCR5b8PIFAUGbuPqVyh7BTKnh9QBP+99NRluyxtGIriLkqio35vX8H1NAMzAIkSSI2M7aQC/di+sUixzurnW3ct6G+oTTzaYa90v4Or1xQgL+rQ74l8IC1YHLXxt74utT818RVo6JNXXcOXkxlR3Qi4zoF2TweFV9QAuYusVbdIUL8XCwCsAxxgHkGE8//cpxcgwlXB1XNdqmbjLBzAez5BJDAp5nF5evduLpXJhDcswgBWAx9mvrSsb4n+y+k8P6maL4Y3brIcWcTsjiXqEWtkNOzSc2pRWcwGYhKjiok9tLy0oocH+gSWKiQcpBbkHDh1kAC3BzyLYH7iU3NZXQF2xfeSXo28eHgxVS2FyUA46qmY8XdThP/Gx1BSoNWZ2TyD0fYfyEFtVLOwsdDK9zjvMrIvG6p7Xdln2X7gQnQbwGoKqeYtkAgKBohAItBJpPx5sNNePjz/1h/Io4JnVN5oG7hwPWC5I8ujbzK3UKuoqTnpRMRH2ETq3c66XSRLlylXGlx4fqGEuobSph/GKG+oXhqSl/yRlD91HJz4O+pDxIdn1WmckXVzUMhvry3MZoD51PI1hmtpUky8wxcTbO096usdm33CgWCOKoUpWDSsvWMX3aIiKsZONkp+W5cm5r7/ji7Ddb8D3JSQO0Mgz6FFsOqe1UCwX2BEIAl0CzAlRFtAvn1cCzz/o5kzXOdkd/yS7qgBMedyP6VJIkrGVdsYvXC48O5lH6pyPGudq42sXqt/FrR1LspdsoqqtQvuKO4adQ19+Z+Gxp4O1LHQ8OV1Bz+O5dM32aWsImCciUBrva4akRW+M009nVGJoNkraWf9O06bcRl5PLk94c4l6jFw1HNDxPa0aJ22ZJG7ggmA+yYD3sXWbb9Wlpcvp7F94kWCASVhxCApeDFPsGsPxFHxNUM/oq4xtCwG8H25xK1xCRkoZTL6F3J7l+9SU9kUmShQsrpeelFjq/rWtcmVi/MP4y6rpXb+1QgqCgymYyHQnxYvu8S/0Yn3hCA8XdXAeg7iYNaQT1PRy4kZxMTn1WkALyYnM0T3x3kWnou/q72/DSpPQ19amCx5PRY+HMSxB60bLedDH3eAVXNj2EVCO4lhAAsBd7OdjzXowEfbo7hg00x9G3mZy30vDm/+HPnhl4Vslqk5abZuG/D48OJTIrEYDYUGquUK2nm3axQcoa7Q82tAScQ3EzPJhYBuCM6EbNZQi6X3dQC7i7IVq0GQvyduZCcTXR8Jg82ss24j7yeydilh0jW6qjn5chPk9oVm7RWbURvhLXPQl462LnC4M+h6eDqXpVAcF8iBGApmdi5HisPXuFqWi7f7LrAjN6W7LSC+L/SZv9KksTljMuFEjMuZ1wucnyBCzfML8wq9pp4NREuXMFdTbt6HjiqFSRm6Th9PZMWtV2JjBMZwMUR4ufCxpPxheIAj1xKZcLyw2TlGWnq78KPk9rh5VTDvh+MevhnDhz40rId0BqGLQWPetW6LIHgfkYIwFJir7KUhXluxTG+2X2eke0C0RnMRMZlopDL6N20sADUGXVEJkUWsuxl6DKKPEaQW5BNL9xQv1DhwhXck9gpFXRp5M3m0/Fsj06gaYALMcIFXCwFnTxuLgWzMyaRZ34+Sp7BTNsgd74f37baEtFuS9ol+GMiXDtq2e7wHPSaC8oq7B8sEAhKRAjAMtC/uR/tgjw4dCmVDzfH0Di/t2anBp4g1/LvxQibxIzIpEiMZmOheVRyFc18mhUSe272bnf4jASC6uOhEB82n45nR3Qij4QGkGcwY6+6u1rA3UkKhPHZBC1Gk5lNp+KZ+Xs4BpNE92Bvvh7zAA7qsrWJq3Ii18FfU0GXAfauMORrCBlY3asSCAQIAVgmCsrCDP5iL2uOX8PBTg+o2Z2wCM8PfynyOe727oVi9Zp4N0GtEL9+Bfc33UO8AThxNYPdZ5IASwu4Gluvrpqp5eaAk50Src7I+5ui+X7vRSQJBoUG8MnwUNTKGlSv06iDrW/CoSWW7dptLS5ft7unXqVAcK8jBGAx6Iw6TiedtnHfRiREoFJMwMnUm1ydGgkTV/UbQQb13evb9MJt5deKQJdA4cIVCIrAx9me0NquRFzN4Ns9lk40wv17e+RyGcF+zhy9nMZ3/1mu15j2dZg3uHnNEs0p5+GPCRAXYdnu/AI89BYoaphrWiC4zxECMJ+UnJRC5VaikqOKdOHa2/2KU25XkOyo46Xn5+F/09K3Ja72NbDelkBQg3koxJeIqxlcS7cUgBYCsHhC8gUgwJQeDZjVJ7hm/cA8tRrWTQN9Fjh4wNBvoHGf6l6VQCAogvtaAP5++nd+OvET4fHhXM28WuQYDwcPmwzcVn6tCPYMZsWBq8xdH8kb/TrTpW7VF4AWCO5Fejbx4f/+OWPdLkh0EBTNI6EB7IxJYuKD9Zj0YA3KoDXkwubX4Ogyy3adjvDY9+Baq3rXJRAIbst9LQDPp55n/Zn11u0G7g0K9cKt5VyryF/Y4zvXY0yHuqgUNSjuRiC4y2gW4IKPsx2JWToAQoQFsFja1/dk76sPVfcybEk+C6vGQ8IpQAZdZkL310FxX99eBIIaz339CR3YeCDOds608mtFS9+WuNiV7eYjxJ9AUDEKuoL8ejiWWm4OuDqIOLG7ihO/w9/TwZANGi94dAk07FndqxIIBKXgvhaALX1b0tK3ZXUvQyC4rxkaVovfj8TSPdi7upciKC36HNj0Mhz/ybId1AUe+w6cS1cQXyAQVD/3tQAUCATVT4Fb08NRlEa6K0iMtrh8k6IAGXR7Bbq9DPIaVoNQIBAUixCAAoGg2vF3dajuJQhKw/EVsOFFMOaCky88+i3U71bdqxIIBOVACECBQCAQFI9OCxtnQUR+wfv63S3iz8mnWpclEAjKjxCAAoFAILg9CactLt/kMyCTQ4/X4cEXQS6S4ASCuxkhAAUCgUBQGEmCYz/AplfAmAfO/pbafkGdq3tlAsF9RW5uLpIkodFoALh8+TJr1qyhadOm9OlT/kLr4iecQCAQCGzJy4Q/J8HfL1jEX8Pe8Mx/QvwJBNXA4MGD+fHHHwFIT0+nffv2fPLJJwwePJivv/663PMKASgQCASCG8RFwJJucOpPkCmg11wY/Ts4elX3ygSC+5Jjx47RpUsXAP744w98fX25fPkyP/74I5999lm55xUuYIFAIBBYXL6Hv4Mtr4NJDy61YdhSqNO+ulcmENzX5OTk4OxsaZO5detWHn30UeRyOR06dODy5cvlnldYAAUCgeB+Jy8DVo2zZPqa9NC4PzyzR4g/gaAG0LBhQ9auXUtsbCxbtmyxxv0lJibi4lL+9plCAAoEAsH9zLWjsLgLRP4FchX0fQ9G/QIaj+pemUAgAN5++21mzZpFUFAQ7du3p2PHjoDFGhgWFlbueYULWCAQCO5HJAkOLoatb4HZAG51YNhyqP1Ada9MIBDcxLBhw3jwwQeJi4sjNDTUur9nz54MHTq03PMKASgQCAT3Gzmp8NdUiNlg2W4yCB75AhzcqnVZAoGgMDt27KBTp074+dn22m7Xrl2F5hUCUCAQCO4nYg/DHxMgIxYUaujzLrSbDDJZda9MIBAUwSOPPILRaKRt27Z0796dbt260blzZxwcKtZCU8QACgQCwf2A2Qx7P4Nl/Sziz70eTNoK7Z8W4k8gqMGkpaWxfft2+vfvz6FDhxg6dChubm507tyZN998s9zzCgEoEAgE9zrZKfDLSNj2FpiN0OxR+N9uCCh/ALlAILgzqFQqOnfuzOuvv86WLVs4cOAAo0aN4tChQyxYsKDc8woBKBAIBPcyl/fDN13g7BZQ2MHD/2ep72df/vIRAsHdwu7duxk0aBABAQHIZDLWrl1rfcxgMPDKK6/QokULHB0dCQgIYOzYsVy/ft1mjqCgIGQymc3f+++/bzPmxIkTdOnSBXt7ewIDA/nwww8LrWXVqlWEhIRgb29PixYt2LhxY6nO4cyZMyxZsoTRo0dTq1YtunXrRkZGBh9//DHHjh0r+0XJR8QACgQCwb2I2Qx7/w92vAuSCTwbwvDl4NeiulcmENwxsrOzCQ0NZeLEiTz66KM2j+Xk5HDs2DHeeustQkNDSUtL44UXXuCRRx7hyJEjNmPnzZvH5MmTrdsFhZkBMjMz6dOnD7169WLx4sWcPHmSiRMn4ubmxtNPPw3Avn37GDVqFAsWLODhhx9m5cqVDBkyhGPHjtG8efNizyEkJARvb29eeOEFXn31VVq0aIGsEsI2hAAUCASCew1tEqx5Gs7vsGy3HAEDF4KdU/WuSyC4w/Tv35/+/fsX+Zirqyvbtm2z2ffFF1/Qrl07rly5Qp06daz7nZ2dC2XhFrBixQr0ej1Lly5FrVbTrFkzwsPDWbhwoVUALlq0iH79+vHSSy8BMH/+fLZt28YXX3zB4sWLiz2HadOmsXv3bubNm8f69evp3r073bt358EHH0Sj0ZT6WtyKcAELBALBvcTFPbD4QYv4UzpYyrsM/UaIP8E9Q1ZWFpmZmdY/nU5XaXNnZGQgk8lwc3Oz2f/+++/j6elJWFgYH330EUaj0frY/v376dq1K2q12rqvb9++xMTEkJaWZh3Tq1cvmzn79u3L/v37S1zTp59+yrFjx4iPj+e1115Dr9fzxhtv4OXlRefOnct9rsICKBAIBPcCZhPs/hh2vQ+SGbyC4fEfwKdJda9MIKhUmjZtarM9e/Zs5syZU+F58/LyeOWVVxg1apRNi7Vp06bRunVrPDw82LdvH6+99hpxcXEsXLgQgPj4eOrVq2czl6+vr/Uxd3d34uPjrftuHhMfH1/q9ZlMJgwGAzqdjry8PHQ6HTExMeU9XSEABQKB4K4nKwFWPwUXd1u2Wz0BAz4EtWP1rksgqAIiIyOpVauWddvOzq7CcxoMBh5//HEkSeLrr7+2eWzmzJnWf7ds2RK1Ws3//vc/FixYUCnHLolp06axc+dOIiMjcXd3p2vXrkyePJnu3bvTokX5Y3qFABQIBIK7mfP/wurJkJ0EKo0lyzd0ZHWvSiCoMpydnW0sdBWlQPxdvnyZHTt2lDh3+/btMRqNXLp0ieDgYPz8/EhISLAZU7BdEDd4uzG3iyu8mbi4OJ5++mm6d+9eYsJIWRACUCAQCO5GTEaLu3f3x4AEPs0sWb7ejat7ZQLBXUOB+Dt79iz//vsvnp6eJT4nPDwcuVyOj48PAB07duSNN97AYDCgUqkA2LZtG8HBwbi7u1vHbN++nenTp1vn2bZtGx07dizxeKtWrSrHmZWMSAIRCASCu43M6/DjI7D7I0CC1uNg8nYh/gSCW9BqtYSHhxMeHg7AxYsXCQ8P58qVKxgMBoYNG8aRI0dYsWIFJpOJ+Ph44uPj0ev1gCV549NPPyUiIoILFy6wYsUKZsyYwRNPPGEVd6NHj0atVjNp0iROnz7Nb7/9xqJFi2xcxy+88AKbN2/mk08+ITo6mjlz5nDkyBGmTp1aqvP46aef6Ny5MwEBAVy+fBmwJIf89ddf5b42QgAKBALB3cTZfyxZvpf3gtoJHvseHvkMVBXrCyoQ3IscOXKEsLAwwsIsXW9mzpxJWFgYb7/9NteuXWPdunVcvXqVVq1a4e/vb/3bt28fYIkv/PXXX+nWrRvNmjXj3XffZcaMGSxZssR6DFdXV7Zu3crFixd54IEHePHFF3n77betJWAAOnXqxMqVK1myZAmhoaH88ccfrF27tlQu3a+//pqZM2cyYMAA0tPTMZlMALi5ufHpp5+W+9oIF7BAIBDcDZgMsOMd2PupZduvBQz/ATwbVOuyBIKaTPfu3ZEk6baPF/cYQOvWrTlw4ECJx2nZsiV79uwpdszw4cMZPnx4iXPdyueff863337LkCFDbDqQtGnThlmzZpV5vgKEABQIBIKaTsZV+GMixB60bLedDH3eAZV99a5LIBBUORcvXrRaMG/Gzs6O7Ozscs9b7S7gL7/8kqCgIOzt7Wnfvj2HDh0qdnx6ejpTpkzB398fOzs7GjduXOp+egKBQHDXEbPJ4vKNPQh2Lhar38CPhfgTCO4T6tWrZ41hvJnNmzfTpEn563xWqwXwt99+Y+bMmSxevJj27dvz6aefWqtnF2TX3Ixer6d37974+Pjwxx9/UKtWLS5fvlyoYrdAIBDc7cjMRuT/vAUH82uSBYTBsGXgUa/4JwoEgnuKmTNnMmXKFPLy8pAkiUOHDvHLL7+wYMECvvvuu3LPW60CcOHChUyePJkJEyYAsHjxYjZs2MDSpUt59dVXC41funQpqamp7Nu3z5pqHRQUdCeXLBAIBFVP+mW6nH0HRc4Fy3aH56DXHFBWfdFZgUBQs3jqqadwcHDgzTffJCcnh9GjRxMQEMCiRYsYObL8NT+rTQDq9XqOHj3Ka6+9Zt0nl8vp1avXbXvjrVu3jo4dOzJlyhT++usvvL29GT16NK+88goKhaLI5+h0Ops+gVlZWYCl9o/BYKjEMxKUlYLrL16HmoF4PWoGsugNKNc/j7suE8nOFdOgz5GCB4AEiNfmjnMvfS4MBgOSZMJsNhU7TpJMNfIeWdPWcycZM2YMY8aMIScnB61WW6SXtKxUmwBMTk7GZDIV2RsvOjq6yOdcuHCBHTt2MGbMGDZu3Mi5c+d47rnnMBgMzJ49u8jnLFiwgLlz5xbav3v3biIjIyt+IoIKs23btupeguAmxOtRPcjNBppd/5X6SZbrn6ppwJF6U8g9D5wXcc7Vzb3wucjKyuL8NTn2js7FjsvLzmJb3nmcnYsfd6dJTk6u7iVUOxqNBo1GUylz3VVZwGazGR8fn/9v787joqreB45/hoFhRyRlc99XFJc0KtdQXNNcsjT3NE1LwdxKhbTULM1yI3f7/jRLMys1FEk0EytNct9RssQNkUWBgbm/P+bLfB1BZIZlWJ736zWvPPeee+5z54g83XPvOaxcuRK1Wk2LFi34559/+Pjjjx+bAE6fPt1oMsZ//vmHhg0b0rZtWxk+tjCtVkt4eDidOnUyDOkLy5H+sKC7Mai3vY7Vrb8A0D49loPap/Hv3FX6wsJK089FfHw8Mb9cxsHZNdd695MS6NSmJm5ubkUTWB5duXLF0iEUmebNmxMREUH58uVp1qwZKpXqsXX//PNPs85hsQSwQoUKqNVqk9bG8/LywsbGxmi4t0GDBoZZuzUaTbZjbG1tjRZrTkxMBMDGxqbE/zCXFtIXxYv0RxE7uQ1+eBvSk8DeDV4KhRodUXbtkr4oRkpDX9jY2KBSqbGyyvmRqSwqlbpYXm9xi6cw9erVy5C79OrVK9cE0FwWSwA1Gg0tWrQgIiKC3r17A/o7fBEREY9dGuW5555j06ZN6HQ6rKz0M9icP38eLy+vHJM/IYQotrSpsHs6HFmrL1f106/qUa6SPOsnRBn38KhmSEhIoZzDovMABgUFsWrVKjZs2MCZM2cYO3YsKSkphreChwwZYvSSyNixY4mPj2fChAmcP3+enTt3MnfuXMaNG2epSxBCCNPdvgir/f+X/D0fBEN36JM/IYR4yOuvv05kZGSBt2vRZwAHDBjArVu3mDVrFnFxcfj6+hIWFmZ4MSQ2NtZwpw+gSpUq7N69m8DAQJo0aUKlSpWYMGECU6dOtdQlCCGEaY5/Az9OBG0KOFSAPl9AbX9LRyWEKKZu3bpFly5dqFixIq+88gqvvfYaTZs2zXe7Fn8JZPz48Y8d8s0p4/Xz88vTunxCCFGspN+Hn6bAsf/oy9XbQJ9V4OJl2biEEMXa999/z927d9myZQubNm1i0aJF1K9fn0GDBjFw4ECzX2i1+FJwQghR6t08C6s6/jf5U0G7qTDke0n+hBB5Ur58eUaPHk1kZCRXr15l2LBh/Oc//6F27dpmt2nxO4BCCFGqHdsIu94B7X1wdIe+q6FmO0tHJYQogbRaLUeOHOG3337jypUr2eZSNkWB3gF88OBBQTYnhBAlV1oyfDcGvn9Tn/zVbA9jf5XkTwhhsn379jFq1Cg8PDwYNmwYLi4u7Nixg2vXrpndZoHcAUxLS2Pp0qV8/PHHxMXFFUSTQghRct04BVuGwe3zoLKC9u9CmyB4wvxrQgjxqEqVKhEfH0+XLl1YuXIlPXv2NJrf2Fx5TgDT0tIICQkhPDwcjUbDlClT6N27N+vWreO9995DrVYTGBiY74CEEKLEUhT480v9yx4ZqeDspR/yrf68pSMTQpRQISEh9O/fH1dX1wJtN88J4KxZs/jiiy/w9/fn0KFD9O/fn+HDh3P48GEWLVpE//79jVboEEKIMiUtST+9y8mt+nJtf3jpC3CsYNGwhBAl26hRowC4ePEily5dom3bttjb26MoSr5WCMlzArhlyxa+/PJLXnzxRU6ePEmTJk3IyMjgr7/+KpQlSoQQosS4flw/5Bt/CVRqeGEmPDsBrGSiBSFE/ty5c4eXX36Zffv2oVKpuHDhAjVr1mTkyJGUL1+ehQsXmtVunv91unbtGi1atACgcePG2NraEhgYKMmfEKLsUhT4Y7V+VY/4S+BSGYb/BM8HSvInhCgQgYGB2NjYEBsbi4ODg2H7gAEDCAsLM7vdPN8BzMzMNFpv19raGicnJ7NPLIQQJVrqPfjhbTi9XV+u2xV6LwcHN4uGJYQoXfbs2cPu3bupXLmy0fY6depw9epVs9vNcwKoKArDhg0zvHmSmprKmDFjcHR0NKq3bds2s4MRQogS4Z8/YetwuHsFrKzB/33wGwcyIiKEKGApKSlGd/6yxMfH5+tt4DwngEOHDjUqv/baa2afVAghSiRFgd++gD0zQKeFclWh/zqo3NLSkQlR4HQ6HQkJCXmq6+rqipU89lAo2rRpw5dffsmcOXMAUKlU6HQ6FixYQIcOHcxuN88J4Lp168w+iRBClHgP7sL34+HsDn25fg/otRTsy1s2LiEKSUJCAot2HMPO0TnXeqkpSQT1aIabmzz+UBgWLFjACy+8wJEjR0hPT2fKlCmcOnWK+Ph4fv31V7PblaXghBDiSa4dgS3D4V4sqDXQ+QNoNVqGfEWpZ+fojKOLq6XDKNMaN27M+fPnWbp0Kc7OziQnJ9OnTx/GjRuHl5f564lLAiiEEI+j08HhZbA3BHQZUL469F8P3s0sHJgQoizQarV06dKF0NBQ3nvvvQJtWxJAIYTIyf14/Vq+F3bry41egp6fgV05y8YlhCgzbGxsOH78eKG0LU9sCiHEo2IPQ+jz+uRPbQvdF0G/dZL8CSGK3GuvvcaaNWsKvF25AyiEEFl0Ovh1Mfz8ASiZ8FRt/ZCvp4+lIxNClFEZGRmsXbuWvXv30qJFi2zT7y1atMisdvOUAP7www95bvDFF180KxAhhLCo5Fvw3RtwKUJf9nkZeiwC29zfgBRCiMJ08uRJmjdvDsD58+eN9hX6WsC9e/fOU2MqlYrMzEyzgxFCCIu4chC2joTkOLC2h24LoNlgectXCGFx+/btK5R285QA6nS6Qjm5EEJYlC4TflkIkfNA0UGFevohX4+Glo5MCCEKlTwDKIQom5JuwLZRELNfX/YdBN0+Bo1j7scJIUQpYFYCmJKSwv79+4mNjSU9Pd1o39tvv10ggQkhRKG5HAnfjoKUm2DjoH/L1/dVS0clhBBFxuQE8NixY3Tr1o379++TkpKCm5sbt2/fxsHBAXd3d0kAhRDFV2YG7P8IDnwMKODeUD/kW7GepSMTQogiZfI8gIGBgfTs2ZO7d+9ib2/P4cOHuXr1Ki1atOCTTz4pjBiFECL/Eq/Dly/CgQWAAs2HwqifJfkTQhQ7zZs35+7duwDMnj2b+/fvF/g5TE4Ao6OjmTRpElZWVqjVatLS0qhSpQoLFizg3XffLfAAhRAi3y7shdDn4OqvoHGCPqvhxc/Bxt7SkQkhCtGBAwfo2bMn3t7eqFQqtm/fbrRfURRmzZqFl5cX9vb2+Pv7c+HCBaM68fHxDBo0CBcXF1xdXRk5ciTJyclGdY4fP06bNm2ws7Mz5ESP2rJlC/Xr18fOzg4fHx927dr12LjPnDlDSkoKAO+//3628xUEk4eAbWxssLLS543u7u7ExsbSoEEDypUrx99//13gAQohhNkyM2DfB3DwU33Zw0c/5FuhtkXDEkIUjZSUFJo2bcqIESPo06dPtv0LFizg888/Z8OGDdSoUYOZM2cSEBDA6dOnsbOzA2DQoEFcv36d8PBwtFotw4cPZ/To0WzatAmAxMREOnfujL+/P6GhoZw4cYIRI0bg6urK6NGjATh06BCvvvoq8+bNo0ePHmzatInevXvz559/0rhx42xx+fr6Mnz4cJ5//nkUReGTTz7Byckpx2ucNWuWWd+NyQlgs2bN+OOPP6hTpw7t2rVj1qxZ3L59m//85z85XoQQQljEvWv6uf3+PqwvP/06dP4QbOwsG5cQosh07dqVrl275rhPURQWL17MjBkz6NWrFwBffvklHh4ebN++nVdeeYUzZ84QFhbGH3/8QcuWLQFYsmQJ3bp145NPPsHb25uNGzeSnp7O2rVr0Wg0NGrUiOjoaBYtWmRIAD/77DO6dOnC5MmTAZgzZw7h4eEsXbqU0NDQbLGtX7+e4OBgduzYgUql4qeffsLaOnvKplKpzE4ATR4Cnjt3Ll5eXgB8+OGHlC9fnrFjx3Lr1i1WrlxpVhBCCFGgzoXp1/L9+zDYuujv+nVfKMmfEKVAUlISiYmJhk9aWppZ7cTExBAXF4e/v79hW7ly5WjdujVRUVEAREVF4erqakj+APz9/bGysuK3334z1Gnbti0ajcZQJyAggHPnzhme44uKijI6T1adrPM8ql69emzevJk//vgDRVGIiIjg2LFj2T5//vmnWdcOZtwBfPhLcHd3JywszOyTCyFEgcpIh4j3IWqpvuzlC/3XgVtNi4YlhCg4DRsaT9QeHBxMSEiIye3ExcUB4OHhYbTdw8PDsC8uLg53d3ej/dbW1ri5uRnVqVGjRrY2svaVL1+euLi4XM+Tm8JajEMmghZClA53r8LWEfDPEX259Vjo9D5Y21o2LiFEgTp9+jSVKlUylG1tS//P+KVLl1i8eDFnzpwB9EnwhAkTqFWrltltmpwA1qhRI9fFhy9fvmx2MEIIYZYzO+D7NyH1HtiVg17LoUEPS0clhCgEzs7OuLi45LsdT09PAG7cuGF4tC2r7Ovra6hz8+ZNo+MyMjKIj483HO/p6cmNGzeM6mSVn1Qna39udu/ezYsvvoivry/PPfccAL/++iuNGjXixx9/pFOnTnm9ZCMmJ4ATJ040Kmu1Wo4dO0ZYWJjh4UYhhCgSGWkQPgt+++9D1JVaQr+1UL6aZeMSQhR7NWrUwNPTk4iICEPCl5iYyG+//cbYsWMB8PPzIyEhgaNHj9KiRQsAfv75Z3Q6Ha1btzbUee+999BqtdjY2AAQHh5OvXr1KF++vKFORESEUQ4VHh6On5/fE+OcNm0agYGBzJ8/P9v2qVOnFl0COGHChBy3L1u2jCNHjpgVhBBCmCz+MmwZDtej9WW/8fBCMFhrcj1MCFF2JCcnc/HiRUM5JiaG6Oho3NzcqFq1KhMnTuSDDz6gTp06hmlgvL296d27NwANGjSgS5cujBo1itDQULRaLePHj+eVV17B29sbgIEDB/L+++8zcuRIpk6dysmTJ/nss8/49NNPDeedMGEC7dq1Y+HChXTv3p3Nmzdz5MiRPL08e+bMGb755pts20eMGMHixYvN/m5Mfgv4cbp27cq3335bUM0JIcTjnfoOvminT/7sy8OrX0PAh5L8CSGMHDlyhGbNmtGsWTMAgoKCaNasmWHqlClTpvDWW28xevRonn76aZKTkwkLCzPMAQiwceNG6tevzwsvvEC3bt14/vnnjRK3cuXKsWfPHmJiYmjRogWTJk1i1qxZhilgAJ599lk2bdrEypUradq0KVu3bmX79u15mj6vYsWKREdHZ9seHR2d7QUVUxTYSyBbt27Fzc2toJoTQojstKmw+104skZfrvIM9FsD5SpbNi4hRLHUvn17FEV57H6VSsXs2bOZPXv2Y+u4ubkZJn1+nCZNmvDLL7/kWqd///70798/94BzMGrUKEaPHs3ly5d59tlnAf0zgB999BFBQUEmt5fFrImgH34JRFEU4uLiuHXrFsuXLzc7ECGEyNXti7BlGNw4oS8/HwQd3gW1jUXDEkKIwjRz5kycnZ1ZuHAh06dPB8Db25uQkBDefvtts9s1OQHs1auXUQJoZWVFxYoVad++PfXr1zc7ECGEeKzjW2DHREhPBocK0OcLqO3/xMOEEKKkU6lUBAYGEhgYSFJSEqB/Ezq/TE4AzZlsUQghzJJ+H8Kmwp9f6svVnoe+q8HFK/fjhBCiFCqIxC+LyS+BqNXqbHPiANy5cwe1Wl0gQQkhBLfOweoX/pv8qaDdVBjyvSR/QghRAEy+A/i4hynT0tKM1sETQgizRW+CnZNAex8c3aHvKqjZ3tJRCSFEqZHnBPDzzz8H9GPRq1evxsnJybAvMzOTAwcOyDOAQoj8SU+Bne/AX/99465GO+izCpw9cj9OCCGESfKcAGZNaKgoCqGhoUbDvRqNhurVqxMaGlrwEQohyoYbp2HLULh9HlRW0H46tJkEVvJoiRCibNJqtXTp0oXQ0FDq1KlToG3nOQGMiYkBoEOHDmzbts2wvIkQQuSLouif8/tpCmSkgrOX/kWP6s9bOjIhhLAoGxsbjh8/Xihtm/wM4L59+wojDiFEWZSWBDsC4cQWfbnWC9BnJThWsGxcQgiz6HQ6EhIS8lTX1dUVK6sCW5Cs1HrttddYs2ZNtrWA88vkBLBv3760atWKqVOnGm1fsGABf/zxB1u2bCmw4IQQpdj147B1ONy5CCo1dJwBz00E+YUgRImVkJDAoh3HsHPMfbqS1JQkgno0kxXE8iAjI4O1a9eyd+9eWrRogaOjo9H+RYsWmdWuyQnggQMHcpwLsGvXrixcuNCsIIQQZYii6JdyC3sXMtPApRL0WwtVn7F0ZEKIAmDn6Iyji6ulwyg1Tp48SfPmzQE4f/680b6HF+YwlckJYHJyco7TvdjY2JCYmGh2IEKIMiD1Hvw4AU59py/X7QK9V4CD3AUQQoicFNajdyaPtfj4+PD1119n275582YaNmxYIEEJIUqhf4/BF231yZ+VNXT+EF7dLMmfEELkwcWLF9m9ezcPHjwAHj8vc16ZfAdw5syZ9OnTh0uXLtGxY0cAIiIi+Oqrr+T5PyFEdooCv6+EPTMgMx3KVYX+66ByS0tHJoQQxd6dO3d4+eWX2bdvHyqVigsXLlCzZk1GjhxJ+fLlzX78zuQ7gD179mT79u1cvHiRN998k0mTJnHt2jX27t1L7969zQpCCFFKPbgLX7+mn+IlMx3q94AxByT5E0KIPAoMDMTGxobY2FgcHBwM2wcMGEBYWJjZ7Zp8BxCge/fudO/ePdv2kydP0rhxY7ODEUKUIteOwJbhcC8WrGyg8wfQ+g3Ix0PLQghR1uzZs4fdu3dTuXJlo+116tTh6tWrZreb7/kWkpKSWLlyJa1ataJp06b5bU4IUdIpChxaAmsD9Mlf+eowcg88M0aSPyGEMFFKSorRnb8s8fHx2Nramt2u2QnggQMHGDJkCF5eXnzyySd07NiRw4cPmx2IEKIUuB8PX72if95PlwENe8MbB6BSc0tHJoQQJVKbNm348ssvDWWVSoVOp2PBggV06NDB7HZNGgKOi4tj/fr1rFmzhsTERF5++WXS0tLYvn27vAEsRFkXexi2joTEa6C2hS5zoeVIuesnhBD5sGDBAl544QWOHDlCeno6U6ZM4dSpU8THx/Prr7+a3W6e7wD27NmTevXqcfz4cRYvXsy///7LkiVLzD6xEKKU0Ongl0Wwrps++XOrBa/vhadfl+RPCCHyqXHjxpw/f57nn3+eXr16kZKSQp8+fTh27Bi1atUyu9083wH86aefePvttxk7dix16tQx+4RCiFIk5TZ89wZc3Ksv+/SHHp+Cbe7LQAkhhMi7cuXK8d577xVom3lOAA8ePMiaNWto0aIFDRo0YPDgwbzyyisFGowQogS58it8OxKSroO1HXT7GJoNlrt+QghRwO7evcuaNWs4c+YMAA0bNmT48OH5Wks5z0PAzzzzDKtWreL69eu88cYbbN68GW9vb3Q6HeHh4SQlJZkdhBCiBNFlwv6PYUMPffJXoS6M2gfNh0jyJ4QQBezAgQNUr16dzz//nLt373L37l0+//xzatSowYEDB8xu1+S3gB0dHRkxYgQHDx7kxIkTTJo0ifnz5+Pu7s6LL75odiBCiBIg6Qb85yXY9wEoOmg6EEZHgoe8BCaEEIVh3LhxDBgwgJiYGLZt28a2bdu4fPkyr7zyCuPGjTO73XzNA1ivXj0WLFjAtWvX+Oqrr/LTlBCiuLscCaHPQ8x+sHGA3ivgpRWgcbR0ZEIIUWpdvHiRSZMmoVarDdvUajVBQUFcvHjR7HbzPRF0ViC9e/fmhx9+KIjmhBDFiS4T9s2FL3tDyk1wb6gf8vUdaOnIhBCi1GvevLnh2b+HnTlzJl8LcJi1FJwQooxIvA7fvg5XD+rLzYdAl49Ak31WeiGEEAXj+PHjhj+//fbbTJgwgYsXL/LMM88AcPjwYZYtW8b8+fPNPockgEKInF3cC9vegPu3QeMEPRZDk/6WjkoIIUo9X19fVCoViqIYtk2ZMiVbvYEDBzJgwACzzlEgQ8D5tWzZMqpXr46dnR2tW7fm999/z9NxmzdvRqVS0bt378INUIiyRJcBe0Pg//rqkz8PHxi9X5I/IYQoIjExMVy+fJmYmJhcP5cvXzb7HBa/A/j1118TFBREaGgorVu3ZvHixQQEBHDu3Dnc3d0fe9yVK1d45513aNOmTRFGK0TpZpd+B/V/esG13/QbWo6EgLlgY2fZwIQQogypVq1aoZ/D4gngokWLGDVqFMOHDwcgNDSUnTt3snbtWqZNm5bjMZmZmQwaNIj333+fX375hYSEhCKMWIjSSXVhDx3OzsAqMwVsXaDnZ9C4j6XDEkKIMu/ff//l4MGD3Lx5E51OZ7Tv7bffNqtNiyaA6enpHD16lOnTpxu2WVlZ4e/vT1RU1GOPmz17Nu7u7owcOZJffvkl13OkpaWRlpZmKGdNWK3VatFqtfm8ApEfWd+/9IOFZWqxivwA68PL9EWPJuj6roHyNUD6xiLkZ6P4KE19odVqUZRMdLrMXOspSqbhek2pn/Ux9RhT4i+L1q9fzxtvvIFGo+Gpp55C9dCE+yqVqmQmgLdv3yYzMxMPDw+j7R4eHpw9ezbHY7KWpIuOjs7TOebNm8f777+fbfuBAwc4ffq0yTGLghceHm7pEMos+/TbtIxZhtv9SwBcqtiZ054D0EWdAbJPOyCKlvxsFB+loS+SkpK49I8Vdo65r9WdmpJEeOp//00wob6zs7PJ53B2zvu64bdv385z3dJk5syZzJo1i+nTp2NlVXCvblh8CNgUSUlJDB48mFWrVlGhQoU8HTN9+nSCgoIM5X/++YeGDRvStm1bqlevXkiRirzQarWEh4fTqVMnbGxsLB1OmaM6twv1jvdRpd5DsStHepdFnLxqI/1RDMjPRvFRmvoiPj6emF8u4+Dsmmu9+0kJdGpTE8Ck+m5ubiafw5S1bK9cuZLnuqXJ/fv3eeWVVwo0+QMLJ4AVKlRArVZz48YNo+03btzA09MzW/1Lly5x5coVevbsadiWNRZubW3NuXPnqFWrltExtra22NraGsqJiYkA2NjYlPgf5tJC+qKIZaRBeDD8tkJfrtQCVb91WDl5w9Vd0h/FiPRF8VEa+sLGxgaVSo2VlTrXeiqV2nCtptTP+ph6jCnxl0UjR45ky5Ytj30vwlwWnQZGo9HQokULIiIiDNt0Oh0RERH4+fllq1+/fn1OnDhBdHS04fPiiy/SoUMHoqOjqVKlSlGGL0TJEx8Dazr/L/nzGw/Dw6B84b9xJoQQRal69eqoVKpsn6z1c9u3b59t35gxY4zaiI2NpXv37jg4OODu7s7kyZPJyMgwqhMZGUnz5s2xtbWldu3arF+/vkCvY968eezfv5/27dvz1ltvERQUZPQxl8WHgIOCghg6dCgtW7akVatWLF68mJSUFMNbwUOGDKFSpUrMmzcPOzs7GjdubHS8q6srQLbtQohHnNoOP7wFaYlgX16/lm+9rpaOSgghCsUff/xBZub/XkY5efIknTp1on///81pOmrUKGbPnm0oOzj8b5WjzMxMunfvjqenJ4cOHeL69esMGTIEGxsb5s6dC+jn6+vevTtjxoxh48aNRERE8Prrr+Pl5UVAQECBXMe8efPYvXs39erVA8j2Eoi5LJ4ADhgwgFu3bjFr1izi4uLw9fUlLCzM8GJIbGxsgY97C1GmaFNhz3vwx2p9uUpr6LcWylW2bFxCiDJPp9PleSq3R6c/eZKKFSsalefPn0+tWrVo166dYZuDg0OOj5wB7Nmzh9OnT7N37148PDzw9fVlzpw5TJ06lZCQEDQaDaGhodSoUYOFCxcC0KBBAw4ePMinn35aYAngwoULWbt2LcOGDSuQ9rJYPAEEGD9+POPHj89xX2RkZK7HFvStViFKlTuXYMtQiDuhLz8fCB3eA3XZfJZGCFG8JCQksGjHsTy9NdzXJ+8vjDwqPT2d//u//yMoKMjortnGjRv5v//7Pzw9PenZsyczZ8403AWMiorCx8fHaKaSgIAAxo4dy6lTp2jWrBlRUVH4+/sbnSsgIICJEyeaHeujbG1tee655wqsvSzFIgEUQhSCE1vhxwmQngwOT8FLK6GO/5OPE0KIImTn6Iyji2ue6yclJRle6ITsL3vmZPv27SQkJBjdRRs4cCDVqlXD29ub48ePM3XqVM6dO8e2bdsAiIuLy3Gauqx9udVJTEzkwYMH2Nvb5/m6HmfChAksWbKEzz//PN9tPUwSQCFKG+0D+Gkq/LlBX672HPRdDS7elo1LCCEKQMOGDY3KwcHBhISE5HrMmjVr6Nq1K97e//t3cPTo0YY/+/j44OXlxQsvvMClS5eyzShiSb///js///wzO3bsoFGjRtnehs5KWE0lCaAQpcmt8/oh35unARW0nQztpoJaftSFEKXD6dOnqVSpkqH8pLt/V69eZe/evU9MlFq3bg3AxYsXqVWrFp6envz+++9GdbKmrct6btDT0zPHqexcXFwK5O4f6F927dOn4JfllN8KQpQW0V/BziDQ3gdHd+izEmp1sHRUQghRoJydnXFxcclz/XXr1uHu7k737t1zrZe1wpiXlxcAfn5+fPjhh9y8eRN3d3dAvyKMi4uL4S6kn58fu3btMmonPDw8x6nszLVu3boCa+th8nqtECVdegpsfxO2j9EnfzXawpiDkvwJIco8nU7HunXrGDp0KNbW/7vndenSJebMmcPRo0e5cuUKP/zwA0OGDKFt27Y0adIEgM6dO9OwYUMGDx7MX3/9xe7du5kxYwbjxo0z3HUcM2YMly9fZsqUKZw9e5bly5fzzTffEBgYaJHrNYXcARSiJLtxGrYMg9vnQGUF7adDm0nwhFn4hRCiLNi7dy+xsbGMGDHCaLtGo2Hv3r2GuYerVKlC3759mTFjhqGOWq1mx44djB07Fj8/PxwdHRk6dKjRvIE1atRg586dBAYG8tlnn1G5cmVWr15dYFPAZJ0jt/n+Ll++bFa7kgAKURIpChz7D+yaAhkPwMlT/6JHjTaWjkwIIYqNzp07oyhKtu1VqlRh//79Tzy+WrVq2YZ4H9W+fXuOHTtmdoxP8uiUMlqtlmPHjhEWFsbkyZPNblcSQCFKmrQk2BEEJ77Rl2t11E/x4lQx9+OEEGbR6XTEx8fnaS1aV1dXWbxAFKgJEybkuH3ZsmUcOXLE7HYlARSiJIk7oR/yvXMRVGroOAOemwjyC0eIQpOSksJnP/2Fg7NrrvVSU5II6tEMNzfzJywWIq+6du3K9OnTzX5JRBJAIUoCRYEjayFsOmSmgUsl6LsGqhXcm2ZCiMezdzJtsmIhCtvWrVvz9T8bkgAKUdylJsKPb8Op7/TlOgHQewU4PmXZuIQQQhS6Zs2aGb0EoigKcXFx3Lp1i+XLl5vdriSAQhRn/x6DLcPhbgxYWYN/CDwzToZ8hRCijOjdu7dR2crKiooVK9K+fXvq169vdruSAApRHCkK/L4S9syAzHQoVxX6rYUqT1s6MiGEEEUoODi4UNqVBFCI4ubBXfh+PJzdoS/X7wG9loJ9ecvGJYQQotSQBFCI4uTaUdg6DBJiwcoGOn8Ard+AXCYBFUIIUfpYWVnlOgE0gEqlIiMjw6z2JQEUojhQFIhaBnuDQZcB5atDv3VQqbmlIxNCCGEB33333WP3RUVF8fnnn6PT6cxuXxJAISztfrx+Ld/zP+nLDXvBi0vArpxl4xJCCGExvXr1yrbt3LlzTJs2jR9//JFBgwYZLUtnKnmVUAhLiv0NQtvokz+1LXRfCP03SPInhBDC4N9//2XUqFH4+PiQkZFBdHQ0GzZsoFq1ama3KXcAhbAEnQ4OfQYRc0DJBLda0H89eDWxdGRCCCGKiXv37jF37lyWLFmCr68vERERtGlTMGu+SwIoRFFLuQ3fjYGL4fpy437QczHYOls0LCGEEMXHggUL+Oijj/D09OSrr77KcUg4PyQBFKIoXfkVvh0JSdfB2g66fgTNh8pbvkIIIYxMmzYNe3t7ateuzYYNG9iwYUOO9bZt22ZW+5IAClEUdJnwyyKInAuKDirU1Q/5ejSydGRCCCGKoSFDhjxxGpj8kARQiMKWfBO2jYLLkfpy01eh2ydg62TRsIQQQhRf69evL9T2JQEUojBd3q9P/pJvgI2DPvFrNsjSUQlRbOl0OhISEvJU19XVFStZF1sIs0gCKERh0GXC/o9g/wJAgYoN9EO+7uYv3C1EWZCQkMCiHcewc8z9pajUlCSCejTDzc2tiCITonSRBFCIgpZ4XX/X78ov+nKzwdB1AWgcLBuXECWEnaMzji6ulg5DiFJNEkAhCtLFCNg2Gu7fBhtH/fQuTV62dFRCCCGEEUkAhSgImRmw70M4uEhf9vDRD/lWqG3RsIQQQoicSAIoRH7d+0c/t19slL7ccgQEzAUbe8vGJYQQQjyGJIBC5Mf5PfDdG/AgHjTO8OLn0LiPpaMSQgghciUJoBDmyNRCxGw49Lm+7NUU+q2Dp2pZNi4hhBAiDyQBFMJUCbGwdQRc+0NfbvUGdJ4D1raWjUsIIYTII0kAhTDF2Z2w/U1ITQDbctBrKTR80dJRCSGEECaRBFCIvMhIh/BZ8NsKfblSC+i3FspXt2hYQgghhDkkARTiSeJjYOtw+PeYvuw3Hl4IBmuNZeMSQgghzCQJoBC5ObUdfngL0hLBzhVeCoV6XS0dlRBCCJEvkgAKkRNtKux5D/5YrS9XaQ1914BrFcvGJYQQQhQASQCFeNSdS7BlGMQd15efmwgdZ4DaxpJRCSGEEAVGEkAhHnZiK/w4AdKTweEpeOkLqNPJ0lEJIYQQBUoSQCEAtA8gbBocXa8vV30W+q0BF2+LhiWEEEIUBkkAhbh1Xj/ke/MUoIK270C7aaCWHw8hhBClk/yGE2XbX5thRxBoU8CxIvRZCbU6WjoqIYQQolBJAijKpvQU2DUFov9PX67RFvqsAmdPy8YlhBBCFAErSwcgRJG7eQZWddQnfyoraP8uDN4uyZ8QQpQiISEhqFQqo0/9+vUN+1NTUxk3bhxPPfUUTk5O9O3blxs3bhi1ERsbS/fu3XFwcMDd3Z3JkyeTkZFhVCcyMpLmzZtja2tL7dq1Wb9+fVFcXr5JAijKDkWBP/8DKzvArbPg5AlDfoD2U8FKbenohBBCFLBGjRpx/fp1w+fgwYOGfYGBgfz4449s2bKF/fv38++//9KnTx/D/szMTLp37056ejqHDh1iw4YNrF+/nlmzZhnqxMTE0L17dzp06EB0dDQTJ07k9ddfZ/fu3UV6neaQIWBRNqQlw45AOPGNvlyrI7y0EpwqWjYuIYQQhcba2hpPz+yjO/fu3WPNmjVs2rSJjh31z32vW7eOBg0acPjwYZ555hn27NnD6dOn2bt3Lx4eHvj6+jJnzhymTp1KSEgIGo2G0NBQatSowcKFCwFo0KABBw8e5NNPPyUgIKBIr9VUcgdQlH5xJ2BlO33yp1LDC7Ng0LeS/AkhRAmUlJREYmKi4ZOWlvbYuhcuXMDb25uaNWsyaNAgYmNjATh69CharRZ/f39D3fr161O1alWioqIAiIqKwsfHBw8PD0OdgIAAEhMTOXXqlKHOw21k1clqoziTBFCUXooCR9bCqhfgzkVw9oZhO6HNJLCSv/pCCFESNWzYkHLlyhk+8+bNy7Fe69atWb9+PWFhYaxYsYKYmBjatGlDUlIScXFxaDQaXF1djY7x8PAgLi4OgLi4OKPkL2t/1r7c6iQmJvLgwYOCuNxCI0PAonRKTdSv6HFqm75cpzP0DgXHpywblxBCiHw5ffo0lSpVMpRtbW1zrNe1a1fDn5s0aULr1q2pVq0a33zzDfb29oUeZ3Ent0FE6fNvNHzRVp/8WVlDpznw6teS/AkhRCng7OyMi4uL4fO4BPBRrq6u1K1bl4sXL+Lp6Ul6ejoJCQlGdW7cuGF4ZtDT0zPbW8FZ5SfVcXFxKfZJpiSAovRQFPhtJazpBHdjoFwVGP4TPPe2DPkKIUQZl5yczKVLl/Dy8qJFixbY2NgQERFh2H/u3DliY2Px8/MDwM/PjxMnTnDz5k1DnfDwcFxcXGjYsKGhzsNtZNXJaqM4kyFgUTo8SIAfxsOZH/Xlet2h11JwcLNoWEIIISzjnXfeoWfPnlSrVo1///2X4OBg1Go1r776KuXKlWPkyJEEBQXh5uaGi4sLb731Fn5+fjzzzDMAdO7cmYYNGzJ48GAWLFhAXFwcM2bMYNy4cYa7jmPGjGHp0qVMmTKFESNG8PPPP/PNN9+wc+dOS156nkgCKEq+a0dh6zBIiAUrG+g8B1qPAZXK0pEJIYSwkGvXrvHqq69y584dKlasyPPPP8/hw4epWFE/A8Snn36KlZUVffv2JS0tjYCAAJYvX244Xq1Ws2PHDsaOHYufnx+Ojo4MHTqU2bNnG+rUqFGDnTt3EhgYyGeffUblypVZvXp1sZ8CBiQBFCWZosDh5RAeDDotuFaD/uugUgtLRyaEEIVOp9Nle4YtJ66urliVwcdgNm/enOt+Ozs7li1bxrJlyx5bp1q1auzatSvXdtq3b8+xY8fMitGSJAEUJdP9eNj+Jpz/SV9u8CK8uATsXS0alhBCFJWEhAQW7TiGnaPzY+ukpiQR1KMZbm7yOIwwJgmgKHlif4OtIyDxGqg1EDAXnn5dhnyFEGWOnaMzji6ulg5DlECSAIqSQ6eDQ59DxGxQMsGtJvRfD15NLR2ZEEIIUaJIAihKhpTb8N0YuBiuLzfuCz0Wg52LRcMSQgghSiJJAEXxd/WQfsg36TpY20HXj6D5UBnyFUIIIcwkCaAovnQ6OLgQ9s0FRQdP1dEP+Xo2tnRkQgghRIkmCaAonpJvwrbRcHmfvtzkFei+EGydLBuXEEIIUQpIAiiKn8v7YdsoSL4B1vbQ/RPwHSRDvkIIIUQBkQRQFB+6TNi/APZ/BChQsYF+yNe9vqUjE0IIIUqVYjE1+LJly6hevTp2dna0bt2a33///bF1V61aRZs2bShfvjzly5fH398/1/qihEiKgy97wf75gALNXoNRP0vyJ4QQQhQCiyeAX3/9NUFBQQQHB/Pnn3/StGlTAgICuHnzZo71IyMjefXVV9m3bx9RUVFUqVKFzp07888//xRx5KKgqC7vgxXPwZVfwMYRXloJvZaBxsHSoQkhhBClksWHgBctWsSoUaMYPnw4AKGhoezcuZO1a9cybdq0bPU3btxoVF69ejXffvstERERDBkypEhiFgVEl0GDf7egPrYDUMCjsX7It0IdS0cmhBBFKq/r+kLZXdtXFCyLJoDp6ekcPXqU6dOnG7ZZWVnh7+9PVFRUntq4f/8+Wq32sescpqWlkZaWZignJSUBoNVq0Wq1+Yhe5Eviv1h9N4q6N34DILPZUHSdPgAbe5B+sYisnwf5ubC8stwXWq0WRclEp8vMtZ6iZBbJv+NZ7WdmKoUaU3x8PJ/99Bf2To9f1xfgQXISE7o2xc3NLU/f1cMxmfrdZv3ZlOsuzHNkZMiLgAXJogng7du3yczMxMPDw2i7h4cHZ8+ezVMbU6dOxdvbG39//xz3z5s3j/fffz/b9gMHDnD69GnTgxb55n7vL5pf/QKbzGS0VnZEVx3BvzwD4fssHZoAwsPDLR2C+K+y2BdJSUlc+scKO8fcE6HUlCTCUy/h7Jx7vYJy5coV7BzvFFpMSUlJ/HvDCrvktFzrpaYkER4ejrOzc56+q4djMvW7BUzui8I8R9S/t3OtI0xj8SHg/Jg/fz6bN28mMjISOzu7HOtMnz6doKAgQ/mff/6hYcOGtG3blurVqxdRpAKATC1WkR+ivrwUAJ2HD/ufGsKzPV7D18bGwsEJrVZLeHg4nTp1wkb6w6LKcl/Ex8cT88tlHJxdc613PymBTm1qPnb0p6BotVq2bdtG9erVcXYtX2gxmXPdeTnG1PoPHwMUSkzmnsOvmjweVJAsmgBWqFABtVrNjRs3jLbfuHEDT0/PXI/95JNPmD9/Pnv37qVJkyaPrWdra4utra2hnJiYCICNjU2Z+4fVohL+1i/ndu2/b2y3Gk1mh2BS9kRIXxQz0h/FR1nsCxsbG1QqNVZW6lzrqVTqIv1+1GpVocZkznXn5RhT6z98TNafCzomc89hbV2i71kVOxZ9ilSj0dCiRQsiIiIM23Q6HREREfj5+T32uAULFjBnzhzCwsJo2bJlUYQq8uPsLgh9Xp/82ZaDl7+Ebh+Dte2TjxVCCCFEgbN4Oh0UFMTQoUNp2bIlrVq1YvHixaSkpBjeCh4yZAiVKlVi3rx5AHz00UfMmjWLTZs2Ub16deLi4gBwcnLCyUmWCStWMtJhbzAcXq4vezeH/uugfHWLhiWEEEKUdRZPAAcMGMCtW7eYNWsWcXFx+Pr6EhYWZngxJDY21uh19xUrVpCenk6/fv2M2gkODiYkJKQoQxe5uXsFtgyHf//Ul58ZB/4hYK2xZFRCCCGEoBgkgADjx49n/PjxOe6LjIw0Kl+5cqXwAxL5c/p7+P4tSLsHdq7QewXU72bpqIQQQgjxX8UiARSlhDYV9syAP1bpy5VbQb+14FrFsnEJIYQQwogkgKJg3LkEW4ZB3HF9+bkJ0HEmqMvWG4xCCCFESSAJoMi/E1vhx4mQngT2bvDSF1C3s6WjEkIIIcRjSAIozKd9AGHT4Oh6fbnqs9B3NZSrZNGwhBBCCJE7SQCFeW5f0A/53jgJqKDNJGg/HdTyV0oIIYQo7uS3tTDdX1/DjkDQpoBjReizEmp1tHRUQgghhMgjSQBF3qXfh12TIfr/9OXqbfRDvs65L9snhBBCiOJFEkCRNzfP6Id8b50FVNB+GrSdDE9Yv1EIIYQQxY8kgCJ3igLRG2HnO5DxAJw89Hf9arS1dGRCCCGEMJMkgOLx0pJhZxAc/1pfrtkB+qwCp4qWjUsIIYQQ+SIJoMhZ3En9kO+dC6Cygg7vwfNB8NC6zEIIIYQomSQBFMYURT+v309TITMNnL2h3xqo9qylIxNCCCFEAZEEUPxPaiLsmAgnv9WXa3fSr+rh+JRFwxJCFA6dTkdCQkKe6rq6umIlIwBClBqSAAq963/ph3zjL4NKDf7B4PeWDPkKUYolJCSwaMcx7Bydc62XmpJEUI9muLm5FVFkQojCJglgWaco8Mdq2P0uZKZDuSrQby1UaWXpyIQQRcDO0RlHF1dLhyGEKGKSAJZlDxLgh7fgzA/6cr1u0GsZOMj/5QshhBClmYzvlVX/HIUv2uqTPysbCJgHr2yS5E8IIUSpMG/ePJ5++mmcnZ1xd3end+/enDt3zqhO+/btUalURp8xY8YY1YmNjaV79+44ODjg7u7O5MmTycjIMKoTGRlJ8+bNsbW1pXbt2qxfv76wLy/fJAEsaxQFopbDmgBIuAqu1WDkbvB7E1QqS0cnhBBCFIj9+/czbtw4Dh8+THh4OFqtls6dO5OSkmJUb9SoUVy/ft3wWbBggWFfZmYm3bt3Jz09nUOHDrFhwwbWr1/PrFmzDHViYmLo3r07HTp0IDo6mokTJ/L666+ze/fuIrtWc8gQcFlyPx6+HwfndunLDV6EF5eAvatFwxJCCCEKWlhYmFF5/fr1uLu7c/ToUdq2/d9qVg4ODnh65rym/Z49ezh9+jR79+7Fw8MDX19f5syZw9SpUwkJCUGj0RAaGkqNGjVYuHAhAA0aNODgwYN8+umnBAQEFN4F5pPcASwr/v5dP+R7bheoNdDtE3j5S0n+hBBClChJSUkkJiYaPmlpaXk67t69ewDZ3mbfuHEjFSpUoHHjxkyfPp379+8b9kVFReHj44OHh4dhW0BAAImJiZw6dcpQx9/f36jNgIAAoqKizLq+oiJ3AEs7nQ6ilkDEbNBlQPka0H89ePtaOjIhhBDCZA0bNjQqBwcHExISkusxOp2OiRMn8txzz9G4cWPD9oEDB1KtWjW8vb05fvw4U6dO5dy5c2zbtg2AuLg4o+QPMJTj4uJyrZOYmMiDBw+wt7c36zoLmySApVnKHdg+Bi7s0Zcb9YGen4Gdi2XjEkIIIcx0+vRpKlWqZCjb2to+8Zhx48Zx8uRJDh48aLR99OjRhj/7+Pjg5eXFCy+8wKVLl6hVq1bBBV0MSQJYWl09BFtHQtK/oLaFrh9Bi2HyoocQQhQBU1dZEXnn7OyMi0veb2SMHz+eHTt2cODAASpXrpxr3datWwNw8eJFatWqhaenJ7///rtRnRs3bgAYnhv09PQ0bHu4jouLS7G9+weSAJY+Oh0cXAT75oKSCU/V0Q/5ejZ+4qFCCCEKhqmrrIiCpygKb731Ft999x2RkZHUqFHjicdER0cD4OXlBYCfnx8ffvghN2/exN3dHYDw8HBcXFwMQ9F+fn7s2rXLqJ3w8HD8/PwK8GoKniSApUnyLdg2Ci7v05ebDIDui8DWybJxCSFEGSSrrFjWuHHj2LRpE99//z3Ozs6GZ/bKlSuHvb09ly5dYtOmTXTr1o2nnnqK48ePExgYSNu2bWnSpAkAnTt3pmHDhgwePJgFCxYQFxfHjBkzGDdunGHoecyYMSxdupQpU6YwYsQIfv75Z7755ht27txpsWvPC0kAS4uYA/Dt65B8A6ztofsn4DtIhnyFEEKUSStWrAD0kz0/bN26dQwbNgyNRsPevXtZvHgxKSkpVKlShb59+zJjxgxDXbVazY4dOxg7dix+fn44OjoydOhQZs+ebahTo0YNdu7cSWBgIJ999hmVK1dm9erVxXoKGJAEsOTTZcKBj2H/R6DooGJ9/ZCvewNLRyaEEEJYjKIoue6vUqUK+/fvf2I71apVyzbE+6j27dtz7Ngxk+KzNEkAS7KkOP2Qb8wBfdn3Nei2ADSOlo1LCCGEEMWaJIAl1aWfYdtoSLkFNo7QYxE0fcXSUQkhhBCiBJAEsKTJzIDIefDLQkAB90b6Id+KdS0dmRBCCCFKCEkAS5J7/+hf9Ig9pC+3GA5d5oFN8Z1nSAghhBDFjySAJcWFcP2Q74N40DhDz8Xg08/SUQkh8siUiYEdHeU5XiFE4ZIEsLjL1MLPc+DXz/Rlzyb6Id+nSvcSNUKUNqZMDPxWgEzcLoQoXJIAFmcJf8PWEXDtv8vQPD0KOn8ANnaWjUsIYRaZGFgIUVxIAlhcnd0F28dCagLYloNeS6BhL0tHJfIpMzMTrVZr6TBypNVqsba2JjU1lczMTEuHU+qkp6fjaAP2Vk/4bm2Kri9MiSk9PZ3U1FRAPzmutbU1KploXogSSxLA4iYjHfaGwOFl+rJ3c+i3FtyevIahKN6Sk5O5du3aEycntRRFUfD09OTvv/+WX+yFQKfT4VdJg0qV+/8AKK4aEhISiqQvTInpzp073L1717DNwcEBLy8vNBpNocUnhCg8kgAWJ3ev6Id8/zmqLz/zJvi/D9byD2xJl5mZybVr13BwcKBixYrFMsHS6XQkJyfj5OSElZWVpcMpdTIyMohPScdKrc61ni4zk/IONjx48KDQ+8KUmNwcNVhbW6MoCunp6dy6dYuYmBjq1Kkjf1+EKIEkASwuTv8A34+HtHtg5wq9V0D9bpaOShQQrVaLoihUrFgRe/viOW2PTqcjPT0dOzs7+YVeCDIyMrDWqlA/IdnKzMzEzk7z3/8Wbl+YFpMt1tb6Xxn29vbY2Nhw9epVw9+ZkkTeyBZCEkDLy0iDPTPg95X6cuWn9UO+rlUtG5coFMXxzp8Q5ijJ/5Mgb2QLIQmgZd25BFuHw/W/9OXnJkDHmaC2sWxcQghRyskb2aKskwTQUk5+Cz9MgPQksHeDl76Aup0tHZUQQgghygBJAIua9gGETYej6/Tlqn7Qdw2Uq2TZuESRM+U5pILi6upaooburly5Qo0aNTh27Bi+vr6WDgeAs2fPMmzYMKKjo6lfvz7R0dFFdu6QkBC2b99epOcUQpROkgAWpdsXYMswuHESUEGbIGj/LqilG8qivD6HVFBSU5II6tEMNze3PB8zbNgwNmzYwLx585g2bZph+/bt23nppZeK7ZQ2hSk4OBhHR0fOnTuHk5NTkZ77nXfe4a233irScwohSifJPIrKX1/DjkDQpoBDBeizEmq/YOmohIWVhOeQ7Ozs+Oijj3jjjTcoX768pcMpEOnp6Ubz1ymKkucJly9dukT37t2pVq2aSecriDuvTk5ORZ50CiFKp5IzFlRSpd+H78fBd6P1yV/1NjD2V0n+RInh7++Pp6cn8+bNe2ydkJCQbEO0ixcvpnr16obysGHD6N27N3PnzsXDwwNXV1dmz55NRkYGkydPxs3NjcqVK7Nu3bps7Z89e5Znn30WOzs7GjduzP79+432nzx5kq5du+Lk5ISHhweDBw/m9u3bhv3t27dn/PjxTJw4kQoVKhAQEGB0fGZmJjcS7hN37wHTZ4ZQpWo1HB0dadqsOV9/t4ObSWncTErDxsaGo0ePMnv2bFQqFSEhITl+H48739nTp3i1by9qelegce1qjB89gjt39HH+Z90amtargU6nM2qrV69ejBgx4rHf8+rVq2nQoAF2dnbUr1+f5cuXG/b169eP8ePHG8oTJ05EpVJx9uxZQJ+Y1q7szoF9PwPw4/ZttPdrSXWP8jSoXon+L3YjJSUlx2ssTnQ6HfHx8U/8PPrdClGWSQJYmG6ehVUd4dj/ASpoNw2GfA/OnpaOTIg8U6vVzJ07lyVLlnDt2rV8tfXzzz/z77//cuDAARYtWkRwcDA9evSgfPny/Pbbb4wZM4Y33ngj23kmT57MpEmTOHbsGH5+fvTs2ZM7d+4A+qH0jh070qxZM44cOUJYWBg3btzg5ZdfNmpjw4YNaDQafv31V0JDQ7PFplKrWbtyBV8sX0LIB/P4+dAfdHihE8MHvczVKzGo1WqOnblEo0aNmDRpEtevX+edd9557LU+er6EhARe7t0dnyZN2R35K199+z23bt5k9NDXAOjZuw934+P59Zf/Jbfx8fGEhYUxaNCgHM+xceNGZs2axYcffsiZM2eYO3cuM2fOZMOGDQC0a9eOyMhIQ/39+/dToUIFw7Y//viDDK2Wlq2f4UbcdcaOHMqrrw3hwO/RbNu5m249e0EJGObPepxi+b6Lj/0s2nGsyJ+5FaI4kwSwMCiKPulb2R5unQEnD33i12E6WOU+4aoQxdFLL72Er68vwcHB+WrHzc2Nzz//nHr16jFixAjq1avH/fv3effdd6lTpw7Tp09Ho9Fw8OBBo+PGjx9P3759adCgAStWrKBcuXKsWbMGgKVLl9KsWTPmzp1L/fr1adasGWvXrmXfvn2cP3/e0EadOnVYsGAB9erVo169ejnGt2LJYsZPmETvfi9Tu05dZs7+kEY+TVi5fCkA7h4eqNVqnJyc8PT0zHU49tHzLV++nMY+TXk3eDZ16tbDp6kvny4L5ddf9nPp4gVcy5enY6fObN+6xdDG1q1bqVChAh06dMjxHMHBwSxcuJA+ffpQo0YN+vTpQ2BgIF988QWgvxN5+vRpbt26xd27dzl9+jQTJkwwJIAHDhygabMWODg4cCMujoyMDLr17EXVatVo0Kgxw0e9gWMJGXLOepzicZ+ietZWiJJCngEsaGnJsHMSHN+sL9dsD31WgZO7RcMSIr8++ugjOnbsmOtdrydp1KiR0bNwHh4eNG78v4l21Wo1Tz31FDdv3jQ6zs/Pz/Bna2trWrZsyZkzZwD466+/2LdvX47J2KVLl6hbty4ALVq0yDW2pMRE4q5f5+ln/Iy2t3rGj1MnTuTxCv/n0fMdP36cQwcPUNO7Qra6V2IuU6t2Hfr0f4V33h5HWpp+LfCvvvqKV155JcfnB1NSUrh06RIjR45k1KhRhu0ZGRmUK1cOgMaNG+Pm5sb+/fvRaDQ0a9aMHj16sGyZvv0DBw7g9/zzADTyaUKbdh3o8OzTtO/oT/uO/vTo9RKupeS5TyGEMUkAC1LcSf3EzrfPg8oKOrwLz0+CEjTthhCP07ZtWwICApg+fTrDhg0z2mdlZZXtjWCtVputDRsb40nOVSpVjttMeVYrOTmZnj178tFHH2Xb5+XlZfhzUS/p9ej5kpOT6RTQlZmz52ar6+6pfyykc9fuKMqb7Nq1i4YNG/LLL7/w6aef5th+cnIyAKtWraJ169ZG+7KWdlOpVLRt25bIyEhsbW1p3749TZo0IS0tjZMnTxIVFcWIMW8Zjvnm+5388VsUkT9HsGblCubNCWFXxAEqV6mSvy9DCFHsSAJYEBQFjq6HsGmQkQrOXvq5/ao/Z+nIhChQ8+fPx9fXN9sQasWKFYmLi0NRFMNydwU5V92hQ4d49tlnAf0drqNHj/Lmm2+SkZGBr68v3333HZUrV8bW1tbs5facXVzw9PLij8NRPPt8G8P23w9H0axFy3xfQ7Nmzdjy7bdUqVbNsKbuo+zs7Oja40W++uorw/fcvHnzHOt6eHjg7e3N5cuXH/uMIOifA1y1ahW2trZ8+OGHWFlZ0bZtWz7++GPS0tJ4uvUzhroqlYpWzzxLq2eeZdLUd2nZuC4/7fieUWPHP7Z9IUTJJAlgfqUmwo6J+pU9AGp3gpdCwTH7MI8Qj0pNSSpR5/Lx8WHQoEF8/vnnRtvbt2/PrVu3WLBgAf369SMsLIyffvoJFxeXfJ8TYOmy5VSsVI06deuxcsVS4u/epUe/V7mZlEb/wSNZtXo1/V5+hfemT8Xd3Z2LFy+yefNmVq9ebbgblhdvvh3Ix/M+oFqNGjRu0pTN//clp04cZ/nq9fm+hrFjx7Jq9WrGjBjCuAlBlC/vRszlS2zftoVFS1YY4nyp/wCGvdqPU6dOMXjw4FzbfP/993n77bcpV64cXbp0IS0tjSNHjnD37l2CgoIAfd8EBgai0Wh4/r/Dve3bt+edd96hZcuWOPz3TuWfR37nl8hI2nV8gQoVK/LnkT+4c/s2derVz/e1CyGKH0kA8+P6X/qJneMvg0oNL8yCZ9+WIV+RJ66urgT1aFbk58yv2bNn8/XXXxtta9CgAcuXL2fu3LnMmTOHvn378s4777By5cp8nw/g3eD3WfbZIk6dOE71mrXY8NVWKrp7AFCpcmV+3LOPObPepVu3bqSlpVGtWjW6dOli8tx7r48ZR2JiIu/PmMbtW7eoW78BGzZvpWat2vm+Bm9vb7b/tJd578/ilZd6kp6eRuUqVeng38kozufbtsPNzY0LFy7w6quv5h7v66/j4ODAxx9/zOTJk3F0dMTHx4eJEyca6vj4+ODq6krdunUNz0m2b9+ezMxM2rVrZ6jn5OzC4UMHWbliKclJiVSuUpXgD+fzQqeAPM+RmBNTVrwpiL+fQoi8kQTQHIoCf6yG3e9CZjq4VIZ+a6Fq6ycfK8R/WVlZmbQqhyWsX78+27bq1auTlpaWbfuYMWMYM2aM0bZ3330317YenqIky5UrV4zOpdVquZmURt+XH58M1axVm9VffoW7s22Ow6s5nScnVlZWvDPtPd6Z9t5j6xw9evSxQ7hPOl/NWrVZu/HrHPc9HMPVq1dJSUnJdgc1JCQk29yDAwcOZODAgbm2Fx8fb7TN19cXRVHIyMjgZpK+L+vWq89X237INTZz5HXFm6yVaoQQRUMSQFOl3oMf3oLT3+vLdbtC7+XgULx/kQshhKWUhBVvhChrJAE0xT9HYctwSLgKVjbQ6X145k0w86FzIYQoSR5eMi8jI8MwvPvwsnpZXF1dC2T5OyFE4ZAEMC8UBX4LhT0zQacF16rQbz1Uzn1eMSFKO1PW0FWr1Wa/oSuKh6wl81RqNRnp6SSlatnzeywpj8z4kzWcW9wfcRCiLJME8Enux8P34+HcTn25QU94cSnYu1o0LCHyIq8Jmilvyj7s4YQg1zgyM/FwdXjis3Mif0zpb3OTcZVajVqtRqdWo1JZYe/oDDpZ4UiIkkb+Nc7N33/oJ3a+9zeoNdD5Q2g1SoZ8hdkenSy5sOUlQctKzswdrstKCHKNw6yWy7asZC4vk2JnJXSm9He+k3HD32X591CIkkgSwJzodBC1BCJmgy4DyteA/uvB29fSkYkSKitBSk9Px97evkjP/aQETZKz4kmn03Hj3n2s1Ln/M/1oQldU/Z2elkqmopCukwRQiJJIEsBHpdyB7WPhwm59uVEf6PkZ2BXMhLaibLK2tsbBwYFbt25hY2NjWDqtsJ+fy8jIICM9HV0uCYEuM5PUVAUrKyvS09NJTU3N893AvLT/8DnMuetUHM+hVqtN7jvTzqEjPT2dvORW+vqpWFtbm9Tfea3/8DEA2vQ0UrVa7ty+xb/3rciUO4BClEhlNgHU6XTZ5sbi7z/0U7wkx4GVBvxn4dp+HFZmPh8lyo68THZrb29PcnIyV65cMax3m3g/HZVV7r9AFZ2Ci4PGrCFanU5HUqoWlerxxyqKjiQ7G1QqFQ8ePMDe3j7PyWZe2n/4HIV1DY+eQ1GUPA23q1QqQ1+Ycg7A5L4z5RyJttY8ePCADCsbrEy4blP629SYDNf9QIuiUvHvfSv+Ts3+9q8QomQoswngvXv3WHswRj85qaLAP0cgNgqUp/UveNTrRuo/dgTduydvshUzpq4skPWLLi/HmFo/65i8Tnabdj+J8Z0bU65cORISEthzKVb/EH0uHqQk8WorL7NWSUhISGDP77mfQ9++J46Ojhw4cIC2bdtiY2NTYO0/fA5XV1d94puYmKf2XVxcSExMNPkcCQkJrI88g62D42Prp91PYVj7Bob6ppwDMLnvTDlH/+YVOH36NNdsq+Dg5JqnmPJ6DlPrP3wMwO7fYrF2KCd3/oQo4cpsAgj/nZzUzgbO7oD4GLDTgEcjqBugf+kjMcHSIebKnESosM8B5Lm+43/XIM3xbmwu5zB1ZQE3N7c8HWNq/YePgbxPdmtjY4OdnR0ajUY/fcYT3qBM0YJGo8HOzu6JbT8qL+d4uP309HTu37+fpwTQ1dXVrGuIj49n6Z5Tef5uzTmHRqMhw8YR21ze1s94pL4p58j6s6kx5fUYGxsb0tPTSbECVQGfw9yYsq77fgY4SvInRIlXLBLAZcuW8fHHHxMXF0fTpk1ZsmQJrVq1emz9LVu2MHPmTK5cuUKdOnX46KOP6Natm+knTvwbTu6H9GSwsoY6ncHLh4J4q60w71JlHWNOImQqcxKhvNZ/K6Cx4RxLdp8slGTrYaYeU9xWLyiKhD8lJYXPfvoLB2fXXOvld9mu4vbdCiFKL1NzjLLC4gng119/TVBQEKGhobRu3ZrFixcTEBDAuXPncHd3z1b/0KFDvPrqq8ybN48ePXqwadMmevfuzZ9//knjxo3zfF6ro+vgTLz+rp/DU9CoNzhWfGz9okjOCvOuUxZTh0LNOUdRJGdlUVEk/AD2TtIXQojSwdQcoyyxeAK4aNEiRo0axfDhwwEIDQ1l586drF27lmnTpmWr/9lnn9GlSxcmT54MwJw5cwgPD2fp0qWEhobm+bxWR9eCfS/wbAJ1/PVDvrkoiuTM3GNMYepQqCheJFEWQoi8MzXHKEssmgCmp6dz9OhRpk+fbthmZWWFv78/UVFROR4TFRVFUFCQ0baAgAC2b9+eY/20tDTS0tIM5Xv37gFwI9WaG+5NsLOrC3/H5nhs6v1krl61ITExkbt373Lv7h2jth5/zFX9Oa5dwc7BKQ/1/3cOU44x9xxPuo6sayjsmGJjVdy+fZvY2Ngiue4nHWOJvigO58iq7+TkRHx8PNczrHF0vmvRmIriHMUxpqxjYmNVJvdFabju4tjfhdkXDx8Dxeu6i2N//2PrAOh/j7u4/G9qNltbW2xtbbMdY06OUaYoFvTPP/8ogHLo0CGj7ZMnT1ZatWqV4zE2NjbKpk2bjLYtW7ZMcXd3z7F+cHCwAshHPvKRj3zkI59S+AkODi6wHKMssfgQcGGbPn260R3D+Ph4atSowcmTJylXrpwFIxNJSUk0bNiQ06dP4+yc+9C6KHzSH8WH9EXxIX1RfNy7d4/GjRsTExNj9IhSTnf/xJNZNAGsUKECarWaGzduGG2/ceMGnp6eOR7j6elpUv3H3RquUqWK0S1kUfSy5oKrVKmS9EUxIP1RfEhfFB/SF8VH1vfv5uaWp74wJ8coS8xb/b2AaDQaWrRoQUREhGGbTqcjIiICPz+/HI/x8/Mzqg8QHh7+2PpCCCGEKHvMyTHKEosPAQcFBTF06FBatmxJq1atWLx4MSkpKYY3doYMGUKlSpWYN28eABMmTKBdu3YsXLiQ7t27s3nzZo4cOcLKlSsteRlCCCGEKGaelGOUZRZPAAcMGMCtW7eYNWsWcXFx+Pr6EhYWhoeHBwCxsbFGE9o+++yzbNq0iRkzZvDuu+9Sp04dtm/fnuc5AG1tbQkODpZnBooB6YviRfqj+JC+KD6kL4oPc/riSTlGWaZSlDysmC6EEEIIIUoNiz4DKIQQQgghip4kgEIIIYQQZYwkgEIIIYQQZYwkgEIIIYQQZUypTACXLVtG9erVsbOzo3Xr1vz++++51t+yZQv169fHzs4OHx8fdu3aVUSRln6m9MWqVato06YN5cuXp3z58vj7+z+x74RpTP3ZyLJ582ZUKhW9e/cu3ADLEFP7IiEhgXHjxuHl5YWtrS1169aVf6sKiKl9sXjxYurVq4e9vT1VqlQhMDCQ1NTUIoq29Dpw4AA9e/bE29sblUrF9u3bn3hMZGQkzZs3x9bWltq1a7N+/fpCj7PUsPRadAVt8+bNikajUdauXaucOnVKGTVqlOLq6qrcuHEjx/q//vqrolarlQULFiinT59WZsyYodjY2CgnTpwo4shLH1P7YuDAgcqyZcuUY8eOKWfOnFGGDRumlCtXTrl27VoRR146mdofWWJiYpRKlSopbdq0UXr16lU0wZZypvZFWlqa0rJlS6Vbt27KwYMHlZiYGCUyMlKJjo4u4shLH1P7YuPGjYqtra2yceNGJSYmRtm9e7fi5eWlBAYGFnHkpc+uXbuU9957T9m2bZsCKN99912u9S9fvqw4ODgoQUFByunTp5UlS5YoarVaCQsLK5qAS7hSlwC2atVKGTdunKGcmZmpeHt7K/Pmzcux/ssvv6x0797daFvr1q2VN954o1DjLAtM7YtHZWRkKM7OzsqGDRsKK8QyxZz+yMjIUJ599lll9erVytChQyUBLCCm9sWKFSuUmjVrKunp6UUVYplhal+MGzdO6dixo9G2oKAg5bnnnivUOMuavCSAU6ZMURo1amS0bcCAAUpAQEAhRlZ6lKoh4PT0dI4ePYq/v79hm5WVFf7+/kRFReV4TFRUlFF9gICAgMfWF3ljTl886v79+2i1WqNFv4V5zO2P2bNn4+7uzsiRI4sizDLBnL744Ycf8PPzY9y4cXh4eNC4cWPmzp1LZmZmUYVdKpnTF88++yxHjx41DBNfvnyZXbt20a1btyKJWfyP/P7OH4uvBFKQbt++TWZmZrYZvj08PDh79myOx8TFxeVYPy4urtDiLAvM6YtHTZ06FW9v72w/4MJ05vTHwYMHWbNmDdHR0UUQYdlhTl9cvnyZn3/+mUGDBrFr1y4uXrzIm2++iVarJTg4uCjCLpXM6YuBAwdy+/Ztnn/+eRRFISMjgzFjxvDuu+8WRcjiIY/7/Z2YmMiDBw+wt7e3UGQlQ6m6AyhKj/nz57N582a+++477OzsLB1OmZOUlMTgwYNZtWoVFSpUsHQ4ZZ5Op8Pd3Z2VK1fSokULBgwYwHvvvUdoaKilQytzIiMjmTt3LsuXL+fPP/9k27Zt7Ny5kzlz5lg6NCFMUqruAFaoUAG1Ws2NGzeMtt+4cQNPT88cj/H09DSpvsgbc/oiyyeffML8+fPZu3cvTZo0KcwwywxT++PSpUtcuXKFnj17GrbpdDoArK2tOXfuHLVq1SrcoEspc342vLy8sLGxQa1WG7Y1aNCAuLg40tPT0Wg0hRpzaWVOX8ycOZPBgwfz+uuvA+Dj40NKSgqjR4/mvffeM1q7XhSux/3+dnFxkbt/eVCq/qZqNBpatGhBRESEYZtOpyMiIgI/P78cj/Hz8zOqDxAeHv7Y+iJvzOkLgAULFjBnzhzCwsJo2bJlUYRaJpjaH/Xr1+fEiRNER0cbPi+++CIdOnQgOjqaKlWqFGX4pYo5PxvPPfccFy9eNCThAOfPn8fLy0uSv3wwpy/u37+fLcnLSswVRSm8YEU28vs7nyz9FkpB27x5s2Jra6usX79eOX36tDJ69GjF1dVViYuLUxRFUQYPHqxMmzbNUP/XX39VrK2tlU8++UQ5c+aMEhwcLNPAFBBT+2L+/PmKRqNRtm7dqly/ft3wSUpKstQllCqm9sej5C3ggmNqX8TGxirOzs7K+PHjlXPnzik7duxQ3N3dlQ8++MBSl1BqmNoXwcHBirOzs/LVV18ply9fVvbs2aPUqlVLefnlly11CaVGUlKScuzYMeXYsWMKoCxatEg5duyYcvXqVUVRFGXatGnK4MGDDfWzpoGZPHmycubMGWXZsmUyDYwJSl0CqCiKsmTJEqVq1aqKRqNRWrVqpRw+fNiwr127dsrQoUON6n/zzTdK3bp1FY1GozRq1EjZuXNnEUdcepnSF9WqVVOAbJ/g4OCiD7yUMvVn42GSABYsU/vi0KFDSuvWrRVbW1ulZs2ayocffqhkZGQUcdSlkyl9odVqlZCQEKVWrVqKnZ2dUqVKFeXNN99U7t69W/SBlzL79u3L8XdA1vc/dOhQpV27dtmO8fX1VTQajVKzZk1l3bp1RR53SaVSFLlnLYQQQghRlpSqZwCFEEIIIcSTSQIohBBCCFHGSAIohBBCCFHGSAIohBBCCFHGSAIohBBCCFHGSAIohBBCCFHGSAIohBBCCFHGSAIohCj2hg0bRu/evQ3l9u3bM3HixCKPIzIyEpVKRUJCQpGfWwghCpIkgEIIswwbNgyVSoVKpUKj0VC7dm1mz55NRkZGoZ9727ZtzJkzJ091izppq169uuF7cXBwwMfHh9WrVxfJuYUQIq8kARRCmK1Lly5cv36dCxcuMGnSJEJCQvj4449zrJuenl5g53Vzc8PZ2bnA2itos2fP5vr165w8eZLXXnuNUaNG8dNPP1k6LCGEMJAEUAhhNltbWzw9PalWrRpjx47F39+fH374AfjfsO2HH36It7c39erVA+Dvv//m5ZdfxtXVFTc3N3r16sWVK1cMbWZmZhIUFISrqytPPfUUU6ZM4dEVKx8dAk5LS2Pq1KlUqVIFW1tbateuzZo1a7hy5QodOnQAoHz58qhUKoYNGwaATqdj3rx51KhRA3t7e5o2bcrWrVuNzrNr1y7q1q2Lvb09HTp0MIozN87Oznh6elKzZk2mTp2Km5sb4eHhJnyzQghRuCQBFEIUGHt7e6M7fREREZw7d47w8HB27NiBVqslICAAZ2dnfvnlF3799VecnJzo0qWL4biFCxeyfv161q5dy8GDB4mPj+e7777L9bxDhgzhq6++4vPPP+fMmTN88cUXODk5UaVKFb799lsAzp07x/Xr1/nss88AmDdvHl9++SWhoaGcOnWKwMBAXnvtNfbv3w/oE9U+ffrQs2dPoqOjef3115k2bZpJ34dOp+Pbb7/l7t27aDQak44VQohCpQghhBmGDh2q9OrVS1EURdHpdEp4eLhia2urvPPOO4b9Hh4eSlpamuGY//znP0q9evUUnU5n2JaWlqbY29sru3fvVhRFUby8vJQFCxYY9mu1WqVy5cqGcymKorRr106ZMGGCoiiKcu7cOQVQwsPDc4xz3759CqDcvXvXsC01NVVxcHBQDh06ZFR35MiRyquvvqooiqJMnz5dadiwodH+qVOnZmvrUdWqVVM0Go3i6OioWFtbK4Di5uamXLhw4bHHCCFEUbO2bPophCjJduzYgZOTE1qtFp1Ox8CBAwkJCTHs9/HxMbrz9ddff3Hx4sVsz++lpqZy6dIl7t27x/Xr12ndurVhn7W1NS1btsw2DJwlOjoatVpNu3bt8hz3xYsXuX//Pp06dTLanp6eTrNmzQA4c+aMURwAfn5+eWp/8uTJDBs2jOvXrzN58mTefPNNateunef4hBCisEkCKIQwW4cOHVixYgUajQZvb2+srY3/SXF0dDQqJycn06JFCzZu3JitrYoVK5oVg729vcnHJCcnA7Bz504qVapktM/W1tasOB5WoUIFateuTe3atdmyZQs+Pj60bNmShg0b5rttIYQoCPIMoBDCbI6OjtSuXZuqVatmS/5y0rx5cy5cuIC7u7shQcr6lCtXjnLlyuHl5cVvv/1mOCYjI4OjR48+tk0fHx90Op3h2b1HZd2BzMzMNGxr2LAhtra2xMbGZoujSpUqADRo0IDff//dqK3Dhw8/8RofVaVKFQYMGMD06dNNPlYIIQqLJIBCiCIzaNAgKlSoQK9evfjll1+IiYkhMjKSt99+m2vXrgEwYcIE5s+fz/bt2zl79ixvvvlmrnP4Va9enaFDhzJixAi2b99uaPObb74BoFq1aqhUKnbs2MGtW7dITk7G2dmZd955h8DAQDZs2MClS5f4888/WbJkCRs2bABgzJgxXLhwgcmTJ3Pu3Dk2bdrE+vXrzbruCRMm8OOPP3LkyBGzjhdCiIImCaAQosg4ODhw4MABqlatSp8+fWjQoAEjR44kNTUVFxcXACZNmsTgwYMZOnQofn5+ODs789JLL+Xa7ooVK+jXrx9vvvkm9evXZ9SoUaSkpABQqVIl3n//faZNm4aHhwfjx48HYM6cOcycOZN58+bRoEEDunTpws6dO6lRowYAVatW5dtvv2X79u00bdqU0NBQ5s6da9Z1N2zYkM6dOzNr1iyzjhdCiIKmUh73ZLUQQgghhCiV5A6gEEIIIUQZIwmgEEIIIUQZIwmgEEIIIUQZIwmgEEIIIUQZIwmgEEIIIUQZIwmgEEIIIUQZIwmgEEIIIUQZIwmgEEIIIUQZIwmgEEIIIUQZIwmgEEIIIUQZIwmgEEIIIUQZIwmgEEIIIUQZ8/+6JE7TjNy5rQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Universal Metric of FSRS: 0.0115\n",
      "Universal Metric of SM2: 0.0808\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def sm2(history):\n",
    "    ivl = 0\n",
    "    ef = 2.5\n",
    "    reps = 0\n",
    "    for delta_t, rating in history:\n",
    "        delta_t = delta_t.item()\n",
    "        rating = rating.item() + 1\n",
    "        if rating > 2:\n",
    "            if reps == 0:\n",
    "                ivl = 1\n",
    "                reps = 1\n",
    "            elif reps == 1:\n",
    "                ivl = 6\n",
    "                reps = 2\n",
    "            else:\n",
    "                ivl = ivl * ef\n",
    "                reps += 1\n",
    "        else:\n",
    "            ivl = 1\n",
    "            reps = 0\n",
    "        ef = max(1.3, ef + (0.1 - (5 - rating) * (0.08 + (5 - rating) * 0.02)))\n",
    "        ivl = max(1, round(ivl+0.01))\n",
    "    return ivl\n",
    "\n",
    "dataset['sm2_interval'] = dataset['tensor'].map(sm2)\n",
    "dataset['sm2_p'] = np.exp(np.log(0.9) * dataset['delta_t'] / dataset['sm2_interval'])\n",
    "cross_comparison = dataset[['sm2_p', 'p', 'y']].copy()\n",
    "\n",
    "R_Metric = mean_squared_error(cross_comparison['y'], cross_comparison['sm2_p'], squared=False) - mean_squared_error(cross_comparison['y'], cross_comparison['p'], squared=False)\n",
    "print(\"R_Metric: \", R_Metric)\n",
    "plot_brier(dataset['sm2_p'], dataset['y'], bins=40)\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(6, 6))\n",
    "\n",
    "cross_comparison['SM2_B-W'] = cross_comparison['sm2_p'] - cross_comparison['y']\n",
    "cross_comparison['SM2_bin'] = cross_comparison['sm2_p'].map(lambda x: round(x, 1))\n",
    "cross_comparison['FSRS_B-W'] = cross_comparison['p'] - cross_comparison['y']\n",
    "cross_comparison['FSRS_bin'] = cross_comparison['p'].map(lambda x: round(x, 1))\n",
    "\n",
    "plt.axhline(y = 0.0, color = 'black', linestyle = '-')\n",
    "\n",
    "cross_comparison_group = cross_comparison.groupby(by='SM2_bin').agg({'y': ['mean'], 'FSRS_B-W': ['mean'], 'p': ['mean', 'count']})\n",
    "print(f\"Universal Metric of FSRS: {mean_squared_error(cross_comparison_group['y', 'mean'], cross_comparison_group['p', 'mean'], sample_weight=cross_comparison_group['p', 'count'], squared=False):.4f}\")\n",
    "cross_comparison_group['p', 'percent'] = cross_comparison_group['p', 'count'] / cross_comparison_group['p', 'count'].sum()\n",
    "plt.scatter(cross_comparison_group.index, cross_comparison_group['FSRS_B-W', 'mean'], s=cross_comparison_group['p', 'percent'] * 1024, alpha=0.5)\n",
    "plt.plot(cross_comparison_group['FSRS_B-W', 'mean'], label='FSRS by SM2')\n",
    "\n",
    "cross_comparison_group = cross_comparison.groupby(by='FSRS_bin').agg({'y': ['mean'], 'SM2_B-W': ['mean'], 'sm2_p': ['mean', 'count']})\n",
    "print(f\"Universal Metric of SM2: {mean_squared_error(cross_comparison_group['y', 'mean'], cross_comparison_group['sm2_p', 'mean'], sample_weight=cross_comparison_group['sm2_p', 'count'], squared=False):.4f}\")\n",
    "cross_comparison_group['sm2_p', 'percent'] = cross_comparison_group['sm2_p', 'count'] / cross_comparison_group['sm2_p', 'count'].sum()\n",
    "plt.scatter(cross_comparison_group.index, cross_comparison_group['SM2_B-W', 'mean'], s=cross_comparison_group['sm2_p', 'percent'] * 1024, alpha=0.5)\n",
    "plt.plot(cross_comparison_group['SM2_B-W', 'mean'], label='SM2 by FSRS')\n",
    "\n",
    "plt.legend(loc='lower center')\n",
    "plt.grid(linestyle='--')\n",
    "plt.title(\"SM2 vs. FSRS\")\n",
    "plt.xlabel('Predicted R')\n",
    "plt.ylabel('B-W Metric')\n",
    "plt.xlim(0, 1)\n",
    "plt.xticks(np.arange(0, 1.1, 0.1))\n",
    "plt.show()"
   ]
  }
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